A structure-based drug repurposing approach by considering the twenty four SARS-CoV2 Targets: A consensus scoring approach

A structure-based drug repurposing approach by considering the twenty four SARS-CoV2 Targets: A consensus scoring approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A structure-based drug repurposing approach by considering the twenty four SARS-CoV2 Targets: A consensus scoring approach Esther Jamir, Himakshi Sarma, Lipsa Priyadarsinee, Kikrusenuo Kiewhuo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2083023/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Drug repurposing is emerging as a vital approach for identifying known drugs with potential therapeutic indications for COVID-19 disease. This aims to categorize and develop therapeutics by identifying existing approved drugs from drug libraries that can effectively reduce drug development time, cost and safety risk. In the current study, virtual screening of known drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-NSP16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). A total of 4193 approved drugs were screened against these targets using AutoDock Vina. The drugs were classified into active and inactive molecules based on the threshold value of the docking score and the therapeutic indications of top 10 and bottom 10 drugs were analyzed in detail. From the study, it was observed that most of the active drugs have antiviral, antibacterial, anticancer, pain and central nervous system based therapeutic properties. The inactive compounds mainly fall in the categories of anti-depressive, vitamin deficiency molecules, and also antiseptics properties. Overall, the outcome of this study will help in identifying the groups of drugs or scaffold that may have activity against COVID-19 targets. SARS-CoV2 targets drug repurposing virtual screening therapeutic indication Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The SARS-CoV2 (severe acute respiratory syndrome coronavirus 2) has caused a pandemic [ 1 – 3 ] and it has affected the lives of millions of people [ 4 ]. The development of therapeutics and antiviral drugs is critical for meeting the challenges currently faced by viral infections [ 5 ]. The SARS-CoV2 pandemic severely collapsed the healthcare system and economy. Though therapies are available for the treatment of SARS-CoV2 infection, they deal with only the symptoms of the disease but not its underlying causes and have not been able to eradicate the disease completely [ 6 – 9 ]. The emergence of new mutant strains may be the primary cause of this increased transmission [ 10 – 11 ]. So far, approximately 30 SARS-CoV2 proteins have been identified [ 12 ], and the occurrence of mutations have increased the rate of infection, replication, transcription and transmission [ 13 – 15 ]. Although SARS-CoV2 shares 79.5% identity with SARS-CoV, the genome sequencing of SARS-CoV2 has shown 96.2% sequence identity to the bat CoV RaTG13 sequence [ 16 ]. The SARS-CoV2 genomic architecture is similar to other CoVs. A 5’ cap and a 3’ poly-A tail is present in the positive-stranded RNA genome structure [ 17 ] in addition to two ORFs i.e., ORF1a and ORF1b. The frameshift between these two ORFs helps in the production of 2 polypeptides that produces 16 different non-structural proteins (NSP 1–16) involved in the viral infection cycle [ 18 ]. Different structural proteins such as S (Spike), N (Nucleoprotein), M (Membrane), and E (Envelope) proteins are encoded at the 3’ end. The N protein binds with the viral genome against the interior of the envelope which is comprised of S, M, and E proteins [ 19 ]. There are nine accessory proteins (ORF3a, ORF3b, ORF6, ORF7a, ORF7b, ORF8, ORF9b, ORF9c, ORF10). These proteins are significant for virulence and host interaction [ 20 – 22 ]. Due to the high sequence similarity among the coronavirus, these proteins could be studied for the development of an antiviral drug. Fig. S1 (a-b) displays the selected proteins of SARS-CoV2 that are involved in RNA synthesis and viral replication. The drug and vaccine development process for the treatment and prevention of COVID-19 has intrigued the scientific community's interest around the world [ 23 , 24 ]. Several vaccines have been developed around the world, but due to the rise of new viral strains, it is still a challenge to eradicate the virus [ 25 ]. Thus, drug repurposing [ 26 , 27 ] approach may have the possibility to identify the potential drug molecules against COVID-19 within a short span of time. Numerous modelling approaches have been applied for repurposing the pre-existing drugs [ 28 – 30 ] to halt the rise of the COVID-19 pandemic. Although there are a few repurposed drugs for COVID-19 available in the market, many limitations were observed with them. For instance, patients under remdesivir treatment were reported to be experiencing adverse events during follow-up. Similarly, few among the repurposed drugs for COVID-19 such as hydroxychloroquine and chloroquine were reported to cause serious heart problems and other safety issues [ 31 , 32 ]. Several approaches such as polypharmacology with drug repurposing is reported to be effective for exploring potential leads and druggable targets for infectious diseases [ 33 ]. The polypharmacology approach involves the identification of small molecules that have activity against multiple targets. This method is widely used for identifying drugs with polypharmacological property for targeting multiple SARS-CoV2 proteins [ 34 ]. In our previous study a combined polypharmacology and drug repurposing approach was applied to screen FDA approved drugs against seven SARS-CoV2 targets. Four drugs (i.e., venetoclax, tirilazad, acetyldigitoxin, and ledipasvir) were chosen based on the docking scores, binding pose and the interaction pattern with the SARS-CoV2 targets [ 35 ]. In arriving at the drug repurposing a consensus scoring approach has been adopted [ 36 ]. Similarly, earlier in the group we have employed and integrated approach involving structure and analog based approaches to arrive at lead molecules [ 37 – 40 ]. All the 30 targets were carefully analyzed and 24 targets were selected based on their functions, binding site pocket size and presence of active site residues. The 24 targets considered in this study are involved in viral entry, RNA synthesis, polyprotein cleavage and replication. The distribution of docking scores for each drug was distributed and analyzed in detail. Figure 1 depicts the schematic workflow used in this study. [Table 1] Materials And Methods Preparation of protein structures The selected 24 SARS-CoV2 proteins were prepared in Autodock tools (ADT version 1.5.7rc1) [ 43 ], where the solvent molecules and non-interacting ions were removed, the partial charges and polar hydrogens were added with the structure. The protein structures were then saved in the PDBQT format. In addition, grid box was generated based on active site and neighbouring residues as shown in Table S1 . Preparation of compound structures The drug molecules were retrieved from DrugBank [ 44 ] and DrugCentral [ 45 ] database. The redundant molecules and molecules containing ions were removed and 4193 unique approved molecules were considered in this study. Further the raccoon.py script of MGL tools was used for optimising the geometry of the compounds, and the compounds were converted to 3D structures and PDBQT format. Virtual screening The virtual screening of the 4193 known drugs against the 24 SARS-CoV2 target was performed using AutodockVina1.1.2. Based on the docking scores the best conformer was identified out of the five conformers generated and the selected complexes were then taken for further analysis. Analysis of most active and least active molecules The docking score of all the drug molecules for each target were arranged in ascending order and the frequency of the docking score was analysed. The maximum no. of drugs that fall in a range of docking scores were considered as “threshold” value and the list of drugs are classified into two different portions. The docking scores which are more than the threshold were considered as active molecules and the docking scores that are less than the threshold was considered as inactive molecules respectively. Further, the top 10 ranked candidates from the active molecule and 10 low-ranking candidates from the inactive molecules were analysed in detail. Distribution of therapeutic indications toward the SARS-CoV2 targets The top 10 and bottom 10 compounds were selected to analyse the most active and least active therapeutic indications for the SARS-CoV2 targets. The therapeutic indication of each compound was retrieved from the drug databases and then the distribution of therapeutic indications toward the SARS-CoV2 targets was carried out for the top 10 and bottom 10 compounds. In addition, the reported FDA approved drugs and drugs under clinical trials for COIVD-19 disease were identified among the top scored drugs in the study. Results Virtual screening of drug molecules against 24 targets Virtual screening of the known drug molecules obtained from DrugBank [44] and DrugCentral database [45] was carried out against the 24 SARS-CoV2 viral targets. The molecules were distributed based on the docking score and a bar graph was plotted for each target to identify the number of drugs falling under different docking scores ( Fig. S2 a-b ). It can be observed that the average maximum docking score for each protein is around -5 kcal/mol to -7 kcal/mol and the least docking scores are around -1 kcal/mol to -4 kcal/mol. The overall docking score distribution showed that the maximum number of the drug showed good binding affinity towards the selected SARS-CoV2 targets. Analysis of drug molecules in the active and inactive dataset The docking cut-off was determined for each target (i.e., the score range that has maximum no. of drugs) and the drug molecules which scored higher than the cut-off was considered as most active molecules whereas the molecules whose docking score was lesser than the cut-off were considered as least active molecules. Table 2 shows the grouping of the total compounds into active and least active groups based on the cut-off scores. The top ten molecules from the most active groups and bottom ten compounds from the least active group were selected for further analysis. From the distribution of active and least active drugs for 24 SARS-CoV2 targets, it can be observed that among the total number of drugs, the average percentage of drugs falling under the most active drugs was found to be 53.5% while the average percentage of drugs falling under the least active drugs was found to be 46.5%. The threshold value was selected based on the highest docking score for each protein distributed across the drug databases. [Table 2] The top 10 drugs showed good binding affinity towards the 24 SARS-CoV2 targets and are listed in Table 3-5 along with their existing therapeutic indication for structural, non-structural and accessory SARS-CoV2 proteins respectively. From the table, it can be observed that among a total number of 240 drugs in the top 10, around 160 drugs were unique and 39 drugs were occurred multiple times in the 24 SARS-CoV2 targets. The most occurring drugs (more than 4 times) across the total 24 targets were dihydroergotamine, ergotamine, midostaurin, temoporfin, tirilazad, venetoclax, paritaprevir, suramin and zorubicin which have therapeutic indications for migraine, immunomodulation, antinematodals, anticancer, antiviral and antibacterial properties ( Fig. 2 ). Several studies have reported that dihydroergotamine and ergotamine are having stable interactions with 3CL pro and also it has the polpypharmacology efficacy [46]. In addition, the drugs tirilazad and venetoclax was reported to show stable binding affinity towards multiple targets for SARS-CoV2 which indicates their polypharmacology property suggesting them as potential candidate for drug repurposing [35, 47]. [Table 3-5] [Fig. 2] Identification of existing approved drugs for COVID-19 According to WHO report (WHO-2019-nCoV-therapeutics-2022) two known drugs namely remdesivir (antiviral compound) and baricitinib (anti-arthritis compound) are approved for treatment of COVID-19. Along with these, several studies have reported a list of drugs ( Table S2) under clinical trials and those used for emergency treatment of COVID-19. In the current study, these known drugs were identified among the drugs interacting with 24 SARS-CoV2 targets. Although, the existing FDA approved drugs and drugs which are under clinical trials for COVID-19 were not found among the top 10 drugs, few of them showed good binding affinity towards the selected SARS-CoV2 targets and have been highlighted along with the top 10 drugs as shown in Fig. 3 and Fig. 4 (a-b) . From the graph it can be observed that the known drugs remdesivir, baricitinib, ritonavir, hydroxychloroquine, lopinavir, favipiravir and dexamethasone having good docking scores due to stable interaction with almost all the 24 SARS-CoV2 targets. The two reported approved drugs for COVID 19, remdesivir and baricitinib was found to be interacting with all the SARS-CoV2 targets except for envelope and OR7a protein. Several studies have reported remdesivir an antiviral drug to inhibit the function of RNA-dependent RNA polymerase (NSP12) with binding score of -5 kcal/mol to -10 kcal/mol [48-51]. From our study it can be compared that remdesivir have a binding score of -7 kcal/mol with NSP12 which confirms the significance of the study. The highest docking score of -8.1 kcal/mol was observed with membrane protein while the lowest docking score of -2.2 kcal/mol with NSP7 and an average binding score of -6.5 kcal/mol across the interacting targets. Similarly, baricitinib an anti-arthritis drug, is reported to interrupt the entry and intracellular assembly of SARS-CoV2 into the host cell and interfere the immune response [52, 53]. Interestingly, from our study it is observed to have a highest of -7.9 kcal/mol with membrane which is involve in humoral response during the SARS-CoV2 and host interaction. Meanwhile, the lowest score of -3.4 kcal/mol was observed for nucleoprotein with an average binding score of -5.92 kcal/mol across the interacting targets some of which are involved in viral entry and immune response. This suggest that some of the existing known drugs for COVID 19 have shown good binding energy along with the top 10 drugs which can be of significant importance for repurposing against multiple SARS-CoV2 targets. [Fig. 3- Fig. 4 (a-b)] Privileged scaffolds identification and their analysis For identifying the privileged scaffolds, a total of 160 unique drugs present in the top 10 drugs across the 24 SARS-CoV2 targets were taken to generate 3 levels of scaffolds. It was observed that from the scaffold tree, level 1, level 2 and level 3 consist of 71, 103 and 123 scaffolds respectively. The identified scaffolds were then compared with the scaffold generated in each level for the existing FDA approved drugs and drugs under clinical trials for COVID-19 disease. From the analysis it was observed that there were 4 common scaffolds (6,7,8,9-Tetrahydro-5H-cyclohepta[c]pyridine, 1-Benzazepine, decalin and leucoline) in level 2 and 2 common scaffolds (4,5,6,7,8,8a,9,10-Octahydro-2(3H)-phenanthrenone, Gona-1,3,5(10)-trien-3-ol) in level 3 of the scaffold tree as shown in Fig. 5 . The compounds present in the level 2 and 3 of scaffold tree generated from the unique compounds can be considered as potential candidates for designing drugs to inhibit the effect of SARS-CoV2. The common scaffolds can also serve as starting point for the development of novel SARS-CoV2 inhibitors. [Fig. 5] Discussion The therapeutic indication of all the molecules was identified through DrugBank and DrugCentral databases and the data was analysed to find the therapeutic indications which can be repurposed against 24 SARS-CoV2 targets. The overall distribution of therapeutic indications of top 10 and bottom 10 drugs across the 24 SARS-CoV2 targets are shown in Fig. 6 . From the graph it can be observed that the drugs with high binding energy were found to have therapeutic indications for CNS (central nervous system), immunology, cardiology, infectious diseases and pain management. Whereas, the drugs with low binding energy were found to have therapeutic indications for immunology, vitamin deficiency, CNS, infectious disease and anaesthesiology. [Fig. 6] Further, the 24 proteins were categorised into five categories based on their role involved in viral biological process viz., cell cycle (Spike, Envelope, Membrane, ORF8), immune response (NSP1, NSP5, NSP15, NSP16, ORF6, ORF7a, ORF9b), replication (NSP3, NSP4, NSP7, NSP8, NSP9, NSP10, NSP12, NSP13, NSP14, Nucleoprotein), cellular degradation (NSP2, NSP6) and pathogenesis (ORF3a). The distribution of potential therapeutic indication of the drugs across the disease area for inhibiting the functions of 24 SARS-CoV2 protein was analysed for top 10 and bottom 10 drugs. It was observed that the top scored drugs for proteins involved in cell cycle showed therapeutic indication for infectious disease and neurology diseases whereas the least scored drugs showed therapeutic indications for oncology and also infectious disease. In case of immune response related proteins, the drugs among the top scored drugs had neurological therapeutic indication and drugs with least score had therapeutic indication for infectious diseases. In case of viral replication processes, the top scored drugs were related to oncology disease area and least scored drugs were related to infectious diseases. The cellular degradation category showed drugs with therapeutic indications for disease areas namely pain among the top, infectious disease among the bottom and for pathogenesis it was observed to be neurology and blood disorder among the top and bottom respectively. Among the structural proteins, the envelope protein plays a vital role in the morphogenesis and assembly of the viral particles [54, 55]. The best molecules for this protein were obtained from different types of therapeutic indications namely liver disease, pain management, diabetes, and hypercortisolism whereas the least binding molecules are from bacterial infection, vitamin B12 deficiency, and immune suppressant agents. The function of the membrane protein is similar to envelope proteins and it is involved in the assembly and morphogenesis of the viral particles [56, 57]. The best active molecules for these targets are mainly from asthma, migraine, and inflammation therapeutic indications, and the least binding molecules are showed therapeutic indications for liver disease, anaemia, and melanoma. Spike protein is important in SARS-CoV2 infection and transmission of viral genetic material into the human [58, 59]. Anticancer, anti-inflammatory and antidiabetic molecules were identified as the best-screened molecules, the molecules that are used for bacterial infection, and immunosuppressants are identified as the least active molecules. The nucleoprotein involves in RNA transcription and viral replication [60, 61]. The antiviral and anticancer molecules were identified as the best active molecules and the anti-depressive and liver disease molecules were considered as the least active molecules. A total of 15 not structural proteins were taken for the study where NSP11 was not considered due to its smaller size and lack of binding cavity. The NSP1 is important for host cell modification [62, 63]. The best molecules are mainly from the drugs used for pain, cardiovascular disease, anticancer, and migraine. The drugs used for bacterial infection and vitamin B12 deficiency are identified as the least active molecules. NSP2 plays important role in viral infection [64, 65]. The virtual screening results revealed that the drug molecules for antibacterial, migraine and chronic hepatitis C are identified under the best active molecules and the least active molecules are mainly from the anaesthetic agent, liver disease, and kidney disease. NSP3 is important for viral replication [66, 67]. The molecules for anticancer, and migraines are among the best active molecules and mainly molecules for liver disease, urinary tract infection and melanoma have fallen in the least active molecules. NSP4 cleaves the replicase polyprotein at the C-terminus at 11 different sites [68, 69]. The radotinib (anticancer molecule) has been identified as the best active compound along with therapeutic indications for asthma, cardiovascular disease etc. The drugs used for hypertension, muscle dysfunction and cystic fibrosis have been identified as the least active molecules. NSP5 (3CL pro ) recognizes substrates containing the core sequence [70, 71]. Drugs used for bacterial infection, hypertension, cancer is identified as the most active molecules. The drugs that are used for depression, fungal infection is identified as the least active molecules. NSP6 plays a vital role in autophagosome and membrane vesicle formation [72-74]. The best active molecules are mainly from cancer treatment, antibacterial, allergy and the least active molecules are from liver disease, hypertension and melanoma. NSP7, NSP8, and NSP9 proteins are majorly involved in viral infection [75-77]. The anticancer, anti-inflammatory and antiviral molecules are identified as the most active molecules and antibacterial drugs, active vitamin D are identified as the least active molecules. NSP10 is majorly involved in viral transcription [78, 79]. The drugs that are used for Parkinson’s disease, hypertension and central nervous system is identified as the most active molecules and drugs that are used for obesity, schizophrenia is screened as the least active molecule. NSP12 helps in viral RNA replication and transcription [80, 81]. Drugs are used for migraines, cancer, and anti-inflammation are identified as the most active molecules. The drugs that are involved in vitamin deficiency, immunosuppressants is identified as the least active molecules. NSP13 with zinc-binding domain in N-terminus is a multi-functional protein [82, 83]. Virtual screening analysis has shown that the anticancer molecules are the most active molecules and the antibacterial molecule was considered as the least active molecule. NSP14 is involved in the unwinding activity of RNA from 5' to 3' polarity [84, 85]. The anticancer molecules with drugs for migraine were identified as the best active molecules and the molecules used for vitamin B12 deficiency, bacterial infection was identified as the least active molecules. NSP15 protein is an endoribonuclease enzyme that cleaves the viral RNA and invades the host immune system [86, 87]. The anticancer, drugs for lung disease and pain-killers were identified as the best active molecules and the molecules that are used as antihypertensive, for cystic fibrosis, and immunosuppressant that are identified as active molecules. NSP16 protein actively participated in viral mRNAs cap methylation [88, 89]. The drugs used for anticancer, central nervous system and pneumonia were among the active molecules and the molecules which are involved in liver disease, antiseptic was considered as the least active molecules. From the accessory proteins selected, the ORF3a protein molecule is involved in controlling up-regulates expression of fibrinogen subunits FGA, FGB, and FGG [90, 91]. The anticancer and antibacterial drugs used for migraine are identified as the most active molecules whereas some molecules that are used for liver disease and ant depressive were identified as the least active molecules. The ORF6 is responsible for multiple viral activities [92, 93]. The drugs used for antibacterial, migraine, anticancer were identified as the best active molecules and the drug molecules that are used as antiseptic and aesthetic were identified as the least active molecules. The ORF7a protein involves in virus replication in cell culture [94, 95]. The most active compounds are pain relievers and anti-diabetic molecules whereas the least active molecules are the drugs for vitamin B12 deficiency. The ORF8 protein is responsible for host-virus interaction [96, 97]. The molecules with therapeutic indications for central nervous system, antiviral, anti-inflammation and anticancer were observed among the most active molecules and the least active molecules were those compounds used for treating cancer. Finally, the ORF9B protein inhibits the host's innate immune response [98, 99]. The most active compounds are majorly for migraine, antibacterial and anticancer and the least active molecules for liver disease and anti-depressive molecules. According to this study, the majority of the active compounds have anticancer, pain, migraine, and anti-inflammatory therapeutic properties, while the least active compounds have anti-depressive, vitamin deficiency molecules, and antiseptics therapeutic properties. This gives an insight into the group of drugs that will can have therapeutic indications towards SARS-CoV2 targets and can be repurposed for developing potential leads for the treatment of COVID 19. In addition, it also narrows downs the category of drugs which need not be considered for repurposing against SARS-CoV2. This indicates that drug repurposing approach can be an effective approach for targeting the multiple targets for SARS-CoV2. Conclusions The current study involves exploring new therapeutic indications of known drugs against SARS-CoV2 targets. Virtual screening was performed to analyse the binding energies of 24 SARS-CoV2 associated proteins and 4193 known drugs. The most active and least active molecules for each target was considered on the basis of docking score. The molecules have high docking scores are considered as the most active molecules and the molecules that have low docking scores are considered as the least active molecules. The most active and least active molecules for each target are categorized on the basis of their therapeutic indications. The results clearly shows that the drugs having anticancer, pain, CNS and anti-inflammatory therapeutic properties have good binding affinity against SARS-CoV2 targets. While anti-depressive, vitamin deficiency molecules, and antiseptics drugs were observed among the drugs with low binding affinity. Through this study, we can note that the drugs among the most active molecules with anticancer, pain, CNS and anti-inflammatory therapeutic indications may be used for targeting the SARS-CoV2 targets through drug repurposing and polypharmacology approach. Hence, the most occurring drugs namely dihydroergotamine, ergotamine, midostaurin, temoporfin, tirilazad, venetoclax, paritaprevir, suramin and zorubicin across the selected targets with therapeutic indications for CNS, immunology, anticancer, antiviral and antibacterial properties may serve to be potential drugs for repurposing against COVID-19 disease. Several studies have found drugs with antiviral, anticancer, and anti-inflammatory properties could be used to for targeting SARS-CoV2 proteins [ 100 ]. Also, the presence of existing approved drugs for COVID-19, namely remdesivir and baricitinib among the drugs interacting with the selected targets, validates the relevance of the study. In addition, the identified common scaffolds generated from the most active molecules may be considered for designing drugs that can effectively disrupt the biological process of SARS-CoV2 thus inhibiting the infection and spread of COVID-19 disease. The study can pave way for effective repurposing for the treatment of disease caused by SARS-CoV2. Declarations Ethics approval and consent to participate Not applicable Consent for publication All the authors declared their consent for publishing this article. Availability of data and materials The data generated from the current study are available from the corresponding author upon request. Competing Interests The authors declare no competing interests. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Authors' contributions The study is conceived by GNS. The calculations have been performed by EJ, HS, LS, KK and NS. All the authors analysed and interpreted the results. All the authors checked the computational results and approved the manuscript as the final version. Acknowledgements GNS thanks DST New Delhi for the J.C Bose fellowship. EJ thanks DST for Inspire Fellowship, KK thanks UGC for JRF Fellowship and HS thanks DBT Centre of Excellence for the fellowship. The DBT is thanked for the funding in the form of Centre of Excellence in Advanced Computation and Data Sciences (No. BT/PR40188/BTIS/137/27/2021). 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Sl.no PDB ID Name of the target Resolution Length Function 1 7K7P Non-Structural Protein 1 1.77 118 Host cell modulation 2 7EXM Non-Structural Protein 2 1.74 278 Cell degradation 3 7LOS Non-Structural Protein 3 (PLpro) 2.9 316 Viral replication 4 Modelled Non-Structural Protein 4 - 499 Cleaves the C-terminus of replicase polyprotein at 11 sites 5 6LU7 Non-Structural Protein 5 (3Clpro) 2.16 30 6 Recognizes substrates containing the core sequence 6 Modelled Non-Structural Protein 6 - 290 Initial induction of autophagosomes from host reticulum endoplasmic 7 6M5I Non-Structural Protein 7 2.5 198 Viral replication 8 7CYQ Non-Structural Protein 8 2.83 942 Viral replication 9 6W4B Non-Structural Protein 9 2.95 117 Viral replication 10 6W4H Non-Structural Protein 10 1.80 142 Viral transcription 11 6M71 Non-Structural Protein 12 (RNA-dependent RNA polymerase) 2.9 942 Replication and Transcription of the viral RNA genome 12 7NN0 Non-Structural Protein 13 (Helicase) 3.04 603 Multi-functional protein having a zinc- binding domain in N-terminus 13 7MC5 Non-Structural Protein 14 (Proofreading exoribonuclease) 1.64 287 Proofreading exoribonuclease for RNA replication 14 6VWW Non-Structural Protein 15(Uridylate-specific endoribonuclease) 2.2 370 Leaves 2'-3'-cyclic phosphates 5' to the cleaved bond 15 6W4H Non-Structural Protein 16 (2'-O-methyltransferase) 1.8 301 Viral mRNAs cap methylation 16 6M0J Spike glycoprotein (S protein) 2.45 603 Viral fusion peptide 17 5X29 Envelope protein (E) - 81 Virus morphogenesis and assembly 18 Modelled Membrane protein (M) - 194 Virus morphogenesis and assembly 19 6WJI Nucleoprotein (N) 2.05 121 RNA transcription and viral replication 20 6XDC Open Reading Frame 3a protein 2.9 284 Up-regulates expression of fibrinogen subunits FGA, FGB and FGG 21 Modelled Open Reading Frame 6 protein - 61 Blocks multiple antiviral activities 22 6W37 Open Reading Frame 7a protein 2.9 67 Virus replication in cell culture 23 7JTL Open Reading Frame 8 protein 2.04 107 Host-virus interaction 24 6Z4U Open Reading Frame 9b protein 1.95 97 Inhibits the host innate immune response Table 2 Distribution of active and inactive drugs across all the 24 SARS-CoV2 proteins. Total number of drugs that has docking score > threshold value has been considered as active (%) control, whereas the drugs that has docking score < threshold value has been considered as inactive (%). Sl.No. Protein Inactive (%) Active (%) Total compounds Threshold value (kcal/mol) 1 Envelope 42.00 58.00 4193 -4 2 Membrane 45.34 54.66 4193 -7 3 Nucleoprotein 42.07 57.93 4193 -7 4 Spike 38.54 61.46 4193 -5.5 5 NSP1 60.98 39.02 4193 -5 6 NSP2 44.77 55.23 4193 -7 7 NSP3 61.65 38.35 4193 -7 8 NSP4 63.44 36.56 4193 -6 9 NSP5 43.72 56.28 4193 -6 10 NSP6 35.20 64.80 4193 -5.5 11 NSP7 44.19 55.81 4193 -3 12 NSP8 48.72 51.28 4193 -5 13 NSP9 59.48 40.52 4193 -6 14 NSP10 28.71 71.29 4193 -6 15 NSP12 41.43 58.57 4193 -6 16 NSP13 25.09 74.91 4193 -6 17 NSP14 43.07 56.93 4193 -6 18 NSP15 49.49 50.51 4193 -6 19 NSP16 50.35 49.65 4193 -6 20 ORF3a 40.45 59.55 4193 -5.5 21 ORF6 34.61 65.39 4193 -5 22 ORF7a 74.74 25.26 4193 -4 23 ORF8 49.84 50.16 4193 -4.5 24 ORF9b 48.20 51.80 4193 -5 Table 3 List of potential top 10 FDA approved drugs for repurposing against structural SARS-CoV2 targets along with their known therapeutic indication. Protein Drug Name Therapeutic Indication Envelope 1 Abiraterone Acetate Liver disease 2 Flufenamic Acid Pain 3 Gliclazide Diabetes mellitus type 2 4 Palonosetron Nausea and vomiting 5 Trilostane Hypercortisolism 6 Florantyrone Dyskinesia 7 Niclosamide Helminthiasis 8 Perampanel Partial seizure 9 Triazolam Insomnia 10 Bentiromide Pancreas Function Membrane 11 Nilotinib Anticancer 12 Ergotamine Migraine 13 Tirilazad CNS 14 Dihydroergotamine Migraine 15 Antrafenine Inflammation 16 Zafirlukast Asthma 17 Midostaurin Leukemia 18 Acetic Acid Susceptible infections 19 Conivaptan Liver disease 20 Pranlukast Asthma Spike 21 Talazoparib Anticancer 11 (a) Nilotinib Anticancer 22 Prednisolone Antiinflamatory 23 Ruboxistaurin Diabetes Mellitus 24 Etravirine HIV-1 infections 25 Piketoprofen Pain 26 Danazol Endometriosis 27 Lumacaftor Cystic fibrosis 28 Raltegravir HIV infections 29 Revaprazan Gastric ulcer Nucleoprotein 30 Paritaprevir Chronic hepatitis C 14 (a) Dihydroergotamine Migraine 31 Temoporfin Anticancer 32 Suramin African trypanosomiasis 12 (a) Ergotamine Migraine 33 Lurbinectedin Anticancer 34 Ledipasvir Antiviral 35 Acetyldigoxin Cardiovascular 19 (a) Conivaptan Liver disease 36 Lanatoside C Cardiovascular *The repetitive occurrence of drug molecule for the different targets are represented by alphabetical order. Table 4 List of potential top 10 FDA approved drugs for repurposing against non-structural SARS-CoV2 targets along with their known therapeutic indication. Protein Drug Name Therapeutic Indication NSP1 37 Thebacon Pain 38 Carbamazepine Bipolar disorder 39 Benmoxin Depression 25 (a) Piketoprofen Pain 40 Imatinib Leukemia 41 Methysergide Migraine 25 (b) Piketoprofen Pain 42 Levosimendan Cardiovascular 43 Plafibride Antimetabolites 44 Prenoxdiazine Cough suppressants NSP2 32 (a) Suramin African trypanosomiasis 45 Teicoplanin Aglycone Antibacterial 12 (b) Ergotamine Migraine 14 (b) Dihydroergotamine Migraine 46 Glecaprevir Chronic hepatitis C 47 Zorubicin None 30 (a) Paritaprevir Chronic hepatitis C 48 Dihydroergocornine Neurotransmitter Agents 49 Cetrorelix Hormone Antagonists 17 (a) Midostaurin Immunomodulating agents NSP3 50 Scopolamine Butylbromide Gastric spasm 51 Pazopanib Anticancer 52 Venetoclax Anticancer 53 Naldemedine Schizophrenia 54 Nafamostat Cystic fibrosis 16 (a) Zafirlukast Asthma 55 Bictegravir HIV infection 56 Bagrosin Anti-epileptic 12 (c) Ergotamine Migraine 57 Rebamipide Anticancer NSP4 58 Radotinib Anticancer 59 Talniflumate cystic fibrosis 60 Netarsudil Ocular hypertension 61 Exatecan Antineoplastic Agents 62 Picloxydine Infection 63 Trenbolone Anabolic Agents 64 Vibegron Muscle dysfunction 20 (a) Pranlukast Asthma 65 Flibanserin Sexual disorder 66 Tasosartan Cardiovascular NSP5 67 Siponimod Relapsing multiple sclerosis 68 Progesterone Contraceptive 69 Cefazolin Bacterial infecton 32 (b) Suramin African trypanosomiasis 66 (a) Tasosartan Antihypertensive 17 (b) Midostaurin Leukemia 21 (a) Talazoparib Anticancer 61 (a) Exatecan Antineoplastic agents 70 Casopitant Urinary incontinence 71 Dactinomycin Sarcoma NSP6 72 Lomitapide Hypercholesterolemia - 73 Moxidectin Infection 74 Diphenadione Antithrombotic agents 75 Avapritinib Gastrointestinal stromal tumor 31 (a) Temoporfin Anticancer 64 (a) Vibegron Muscle dysfunction 76 Tadalafil Benign prostatic hyperplasia 77 Phenolsulfonphthalein Renal function study 78 Bisdequalinium Chloride Antibacterial 79 Ketotifen Allergic conjunctivitis NSP7 80 Desloratadine Allergy 81 Mianserin depressive disorder 82 Setiptiline Depressive disorder 83 Mecloqualone Insomnia 84 Metapramine Antidepressive Agents 85 Cyproheptadine allergic symptoms 2 (a) Flufenamic Acid Pain 86 Methaqualone Antidepressive 87 Oxcarbazepine Depressive disorder 88 Lorajmine Cardiovascular NSP8 78 (a) Bisdequalinium Chloride Antibacterial 89 Alpelisib Cancer 90 Niraparib tumor of ovary 91 Risdiplam Spinal muscular atrophy 92 Fenoverine irritable bowel syndrome. 93 Irinotecan cancer 94 Calcipotriol psoriasis 14 (c) Dihydroergotamine Migraine 6 (a) Florantyrone Dyskinesia 95 Paliperidone Schizophrenia NSP9 31 (b) Temoporfin Anticancer 93 (a) Irinotecan cancer 47 (a) Zorubicin Anticancer 96 Ciclesonide allergic rhinitis 91 (a) Risdiplam Spinal muscular atrophy 34 (a) Ledipasvir Antiviral 32 (c) Suramin African trypanosomiasis 52 (a) Venetoclax Anticancer 59 (a) Talniflumate Anti-inflammatory NSP10 97 Dihydroergocristine Progressive mental decline 98 Ergometrine Postpartum haemorrhage 99 Ubidecarenone Congestive heart failure 100 Tacrine CNS 101 Perflubron Hemorrhage 102 Rasagiline Parkinson's disease 103 Talastine Antihistamine 104 Barnidipine Hypertension 105 Glasdegib Acute myeloid leukemia 106 Lercanidipine Hypertensive disorder NSP12 14 (d) Dihydroergotamine Migraine 12 (d) Ergotamine Migraine 107 Trabectedin tumor of ovary 108 Carsalam anti-inflammatory 109 Mosapramine Schizophrenia 110 Amrubicin lung cancer 111 Betamethasone Phosphate Anti-inflamation 112 Rimegepant Migraine 17 (c) Midostaurin leukemia 113 Pirarubicin Immunomodulating agents NSP13 51 (a) Pazopanib Anticancer 52 (b) Venetoclax Anticancer 114 Racepinefrine Hypertensive disorder 16 (b) Zafirlukast Asthma 55 (a) Bictegravir HIV infection 115 Prednisolone Succinate anaphylaxis, asthma 12 (e) Ergotamine Migraine 57 (a) Rebamipide Anticancer 116 Tucatinib Anticancer 117 Steviolbioside Antitubercular agent NSP14 112 (a) Rimegepant Migraine 117 (a) Tucatinib Anticancer 17 (d) Midostaurin Leukemia 58 (a) Radotinib Anticancer 118 Fosaprepitant Nausea and vomiting 119 Alatrofloxacin Bacterial infections 27 (a) Lumacaftor Cystic fibrosis 120 Lonafarnib Hutchinson-Gilford syndrome 121 Nicomorphine Analgesic 122 Tacalcitol Plaque psoriasis NSP15 27 (b) Lumacaftor Cystic fibrosis 123 Fazadinium Pain 124 Evocalcet Secondary hyperparathyroidism 125 Ivosidenib Acute myeloid leukemia 66 (b) Tasosartan Antihypertensive 126 Fluspirilene Schizophrenia 127 Olodaterol Lung disease 128 Argatroban Thrombosis 129 Hesperidin Hemorrhoids 139 Quercetin Kidney disease NSP16 131 Sonidegib Basal cell carcinoma of skin 62 (a) Picloxydine Infection 132 Flavin Adenine Dinucleotide vitamin B2 deficiency 133 Gitoxin Anticancer 134 Ceftobiprole Medocaril Pneumonia 47 (b) Zorubicin Anticancer 53 (a) Naldemedine Schizophrenia 135 Enantate Benzilic Acid Hormonal deficiency 13 (a) Tirilazad CNS 136 Berotralstat Angioneurotic edema *The repetitive occurrence of drug molecule for the different targets are represented by alphabetical order. Table 5 List of potential top 10 FDA approved drugs for repurposing against accessory SARS-CoV2 targets along with their known therapeutic indication. Protein Drug Name Therapeutic Indication ORF3a 78 (b) Bisdequalinium Chloride Antibacterial 137 Dutasteride Benign prostatic hyperplasia 31 (c) Temoporfin Anticancer 13 (b) Tirilazad CNS 12 (f) Ergotamine Migraine 14 (e) Dihydroergotamine Migraine 45 (a) Teicoplanin Aglycone Antibacterial 11 (b) Nilotinib Anticancer 138 Revefenacin Chronic obstructive lung disease 139 Adapalene Dermatology ORF6 78 (c) Bisdequalinium Chloride Antibacterial 13 (c) Tirilazad CNS 31 (d) Temoporfin Anticancer 140 Vaniprevir Chronic hepatitis C 139 (a) Adapalene Dermatology 141 Saquinavir HIV infections 19 (b) Conivaptan Liver disease 14 (f) Dihydroergotamine Migraine 126 (a) Fluspirilene Schizophrenia 30 (b) Paritaprevir Chronic hepatitis C ORF7a 142 Hydroflumethiazide Hypertensive disorder 143 Naftazone Hemorrhoids 144 Pomalidomide Multiple myeloma 145 Tramazoline nasal decongestion 146 Dezocine Pain 147 Huperzine A Alzheimer's disease 148 Hydromorphone Pain 149 Naproxen Pain 150 Phenformin Type II diabetes mellitus 151 Primidone Epilepsy ORF 8 13 (d) Tirilazad CNS 152 Pirenoxine Cataract 152 Vesnarinone Antiviral agents 154 Magnesium Orotate Mineral supplements 155 Tropatepine CNS 37 (a) Thebacon Respiratory system 156 Enoxolone Anti-inflammatory agents 52 (c) Venetoclax Anticancer 157 Traxanox Immunosuppressive Agents 47 (c) Zorubicin Immunomodulation agent ORF 9b 30 (c) Paritaprevir Chronic hepatitis C 12 (g) Ergotamine Migraine 78 (d) Bisdequalinium Chloride Antibacterial 158 Ubrogepant Migraine 14 (g) Dihydroergotamine Migraine 109 (a) Mosapramine Schizophrenia 52 (d) Venetoclax Anticancer 53 (b) Naldemedine Schizophrenia 159 Netupitant Nausea and vomiting 160 Telmisartan Hypertensive disorder *The repetitive occurrence of drug molecule for the different targets are represented by alphabetical order. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2083023","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":139164843,"identity":"9494dcc9-c873-4e69-88a6-4431fc41e759","order_by":0,"name":"Esther Jamir","email":"","orcid":"","institution":"North East Institute of Science and Technology CSIR","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Esther","middleName":"","lastName":"Jamir","suffix":""},{"id":139164844,"identity":"01306a40-a07d-4178-969a-66f9d349c9fb","order_by":1,"name":"Himakshi Sarma","email":"","orcid":"","institution":"North East Institute of Science and Technology CSIR","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Himakshi","middleName":"","lastName":"Sarma","suffix":""},{"id":139164845,"identity":"92bbc332-772d-4ffe-8b9b-e77db9433cae","order_by":2,"name":"Lipsa Priyadarsinee","email":"","orcid":"","institution":"North East Institute of Science and Technology CSIR","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lipsa","middleName":"","lastName":"Priyadarsinee","suffix":""},{"id":139164846,"identity":"af3ce9c2-5842-441a-bc24-3feb0506254f","order_by":3,"name":"Kikrusenuo Kiewhuo","email":"","orcid":"","institution":"North East Institute of Science and Technology CSIR","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kikrusenuo","middleName":"","lastName":"Kiewhuo","suffix":""},{"id":139164847,"identity":"992bcd44-455e-4759-a4c2-c971dce33bc8","order_by":4,"name":"Selvaraman Nagamani","email":"","orcid":"","institution":"North East Institute of Science and Technology CSIR","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Selvaraman","middleName":"","lastName":"Nagamani","suffix":""},{"id":139164848,"identity":"0a8d0c09-8e74-49d2-a066-ef6e43990760","order_by":5,"name":"G. Narahari Sastry","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDADfhCRUEC0+gQGBskGEG1AihaDAyAGMVrko9sffvz5417i5vOrEz88MGCQ5xc7gF+L4Z0zxtI8CcWJ22683SwBdJjhzNkJBLTMyGGQZkhIAGo5uwGkJcHgNkEt6Y9//gBq2Tzj7OYfRGmRl0gwk+ABatnA37uNOFsMJHLMrHnSEoxn3ODdZpFgIEHYL/JAh938YZMg299/dvPNHxU28vzShGw5AKEdGyTAKiXwKwfb0gCh7Rn4DxBWPQpGwSgYBSMTAACMl0eAnH8GXwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3181-7673","institution":"North East Institute of Science and Technology CSIR","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"G.","middleName":"Narahari","lastName":"Sastry","suffix":""}],"badges":[],"createdAt":"2022-09-20 04:12:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2083023/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2083023/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27101414,"identity":"bc1bada1-38d6-407d-9df4-e79067f840c4","added_by":"auto","created_at":"2022-09-28 19:24:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154867,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic workflow of screening known drugs for therapeutic indications against SARS-CoV2 selected targets.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/3bb36354a74163e08f692c12.png"},{"id":27101159,"identity":"b64b4efa-9e50-4ae7-9fa0-bdabb8180073","added_by":"auto","created_at":"2022-09-28 19:19:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":340961,"visible":true,"origin":"","legend":"\u003cp\u003e2D structures of the most occurring FDA approved drugs across the 24 SARS-CoV2 targets\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/3dd74cfd8e52c15ad1e28a67.png"},{"id":27101161,"identity":"b3acbfeb-8736-43bd-a695-97607e2e3dd6","added_by":"auto","created_at":"2022-09-28 19:19:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":594266,"visible":true,"origin":"","legend":"\u003cp\u003eThe binding score distribution of top 10 FDA approved drugs against SARS-CoV2 targets that are structural proteins namely A) Envelope B) Membrane C) Spike D) Nucleoprotein and accessory proteins E) ORF3a F) ORF6 G) ORF7a H) ORF8 and I) ORF9. The drugs which are approved and under clinical trials for COVID-19 showing binding affinity against the selected targets have been highlighted (*) in the graph.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/8ed71494240825959c1caa32.png"},{"id":27100723,"identity":"457d43de-808a-47b0-85ab-3a2a6af6e08c","added_by":"auto","created_at":"2022-09-28 19:14:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1283113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e The binding score distribution of top 10 FDA approved drugs against SARS-CoV2 targets that are non-structural proteins namely A) NSP1 B) NSP2 C) NSP3 D) NSP4 E) NSP5 F) NSP6 G) NSP7 and H) NSP8. The drugs which are approved and under clinical trials for COVID-19 showing binding affinity towards the selected targets have been highlighted (*) in the graph.\u0026nbsp;\u003cstrong\u003e(b)\u003c/strong\u003e The binding score distribution of top 10 FDA approved drug against the SARS-CoV2 targets that are non-structural namely I) NSP 9 J) NSP10 K) NSP12 L) NSP13 M) NSP14 N) NSP15 and O) NSP26. The drugs which are approved and under clinical trials for COVID-19 showing binding affinity towards the selected targets have been highlighted (*) in the graph.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/e0096d99c3bf81a5e042b90f.png"},{"id":27100726,"identity":"ca901a9f-6f59-4245-a1b9-518fd708b921","added_by":"auto","created_at":"2022-09-28 19:14:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125558,"visible":true,"origin":"","legend":"\u003cp\u003eThe level 2 and level 3 common scaffolds that are present among the top scored drug molecules across all the 24 SAR-CoV2 targets, existing approved drugs and drugs under clinical trials for COVID-19.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/dfcf5cb983615d9691f13647.png"},{"id":27100725,"identity":"cf56ce65-f671-4e3d-a074-3a6272f7d2f9","added_by":"auto","created_at":"2022-09-28 19:14:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27331,"visible":true,"origin":"","legend":"\u003cp\u003eThe A) good docking score B) poor docking score compounds are classified based on the top five therapeutic areas. The figure depicts that the good docking score compounds are mainly CNS drugs whereas poor docking score molecules are mainly from immune related disease.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/2a2399ff0e0d875657943044.png"},{"id":30816506,"identity":"73e2953c-1353-475b-9d8b-7b4a32b5fff9","added_by":"auto","created_at":"2022-12-27 22:44:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1430099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/ce409267-ae1d-44cf-801e-3dccde360edd.pdf"},{"id":27100720,"identity":"15206255-f8a9-4392-bb7d-d4478eb8dec5","added_by":"auto","created_at":"2022-09-28 19:14:51","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":116395,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/561c5df6c115a9e144d9274b.png"},{"id":27100727,"identity":"c4887b7c-dd5d-4d37-becb-dca143ba809b","added_by":"auto","created_at":"2022-09-28 19:14:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4436821,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2083023/v1/4bf71d0c6e90962fc15ae1fa.docx"}],"financialInterests":"","formattedTitle":"A structure-based drug repurposing approach by considering the twenty four SARS-CoV2 Targets: A consensus scoring approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe SARS-CoV2 (severe acute respiratory syndrome coronavirus 2) has caused a pandemic [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] and it has affected the lives of millions of people [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. The development of therapeutics and antiviral drugs is critical for meeting the challenges currently faced by viral infections [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. The SARS-CoV2 pandemic severely collapsed the healthcare system and economy. Though therapies are available for the treatment of SARS-CoV2 infection, they deal with only the symptoms of the disease but not its underlying causes and have not been able to eradicate the disease completely [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. The emergence of new mutant strains may be the primary cause of this increased transmission [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. So far, approximately 30 SARS-CoV2 proteins have been identified [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], and the occurrence of mutations have increased the rate of infection, replication, transcription and transmission [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although SARS-CoV2 shares 79.5% identity with SARS-CoV, the genome sequencing of SARS-CoV2 has shown 96.2% sequence identity to the bat CoV RaTG13 sequence [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. The SARS-CoV2 genomic architecture is similar to other CoVs. A 5\u0026rsquo; cap and a 3\u0026rsquo; poly-A tail is present in the positive-stranded RNA genome structure [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] in addition to two ORFs i.e., ORF1a and ORF1b. The frameshift between these two ORFs helps in the production of 2 polypeptides that produces 16 different non-structural proteins (NSP 1\u0026ndash;16) involved in the viral infection cycle [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Different structural proteins such as S (Spike), N (Nucleoprotein), M (Membrane), and E (Envelope) proteins are encoded at the 3\u0026rsquo; end. The N protein binds with the viral genome against the interior of the envelope which is comprised of S, M, and E proteins [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. There are nine accessory proteins (ORF3a, ORF3b, ORF6, ORF7a, ORF7b, ORF8, ORF9b, ORF9c, ORF10). These proteins are significant for virulence and host interaction [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the high sequence similarity among the coronavirus, these proteins could be studied for the development of an antiviral drug. \u003cstrong\u003eFig. S1 (a-b)\u003c/strong\u003e displays the selected proteins of SARS-CoV2 that are involved in RNA synthesis and viral replication.\u003c/p\u003e\n\u003cp\u003eThe drug and vaccine development process for the treatment and prevention of COVID-19 has intrigued the scientific community\u0026apos;s interest around the world [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Several vaccines have been developed around the world, but due to the rise of new viral strains, it is still a challenge to eradicate the virus [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, drug repurposing [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] approach may have the possibility to identify the potential drug molecules against COVID-19 within a short span of time. Numerous modelling approaches have been applied for repurposing the pre-existing drugs [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] to halt the rise of the COVID-19 pandemic. Although there are a few repurposed drugs for COVID-19 available in the market, many limitations were observed with them. For instance, patients under remdesivir treatment were reported to be experiencing adverse events during follow-up. Similarly, few among the repurposed drugs for COVID-19 such as hydroxychloroquine and chloroquine were reported to cause serious heart problems and other safety issues [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Several approaches such as polypharmacology with drug repurposing is reported to be effective for exploring potential leads and druggable targets for infectious diseases [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The polypharmacology approach involves the identification of small molecules that have activity against multiple targets. This method is widely used for identifying drugs with polypharmacological property for targeting multiple SARS-CoV2 proteins [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our previous study a combined polypharmacology and drug repurposing approach was applied to screen FDA approved drugs against seven SARS-CoV2 targets. Four drugs (i.e., venetoclax, tirilazad, acetyldigitoxin, and ledipasvir) were chosen based on the docking scores, binding pose and the interaction pattern with the SARS-CoV2 targets [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. In arriving at the drug repurposing a consensus scoring approach has been adopted [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similarly, earlier in the group we have employed and integrated approach involving structure and analog based approaches to arrive at lead molecules [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. All the 30 targets were carefully analyzed and 24 targets were selected based on their functions, binding site pocket size and presence of active site residues. The 24 targets considered in this study are involved in viral entry, RNA synthesis, polyprotein cleavage and replication. The distribution of docking scores for each drug was distributed and analyzed in detail. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the schematic workflow used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 1]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of protein structures\u003c/h2\u003e \u003cp\u003eThe selected 24 SARS-CoV2 proteins were prepared in Autodock tools (ADT version 1.5.7rc1) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], where the solvent molecules and non-interacting ions were removed, the partial charges and polar hydrogens were added with the structure. The protein structures were then saved in the PDBQT format. In addition, grid box was generated based on active site and neighbouring residues as shown in \u003cb\u003eTable S1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of compound structures\u003c/h2\u003e \u003cp\u003eThe drug molecules were retrieved from DrugBank [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and DrugCentral [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] database. The redundant molecules and molecules containing ions were removed and 4193 unique approved molecules were considered in this study. Further the raccoon.py script of MGL tools was used for optimising the geometry of the compounds, and the compounds were converted to 3D structures and PDBQT format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVirtual screening\u003c/h2\u003e \u003cp\u003eThe virtual screening of the 4193 known drugs against the 24 SARS-CoV2 target was performed using AutodockVina1.1.2. Based on the docking scores the best conformer was identified out of the five conformers generated and the selected complexes were then taken for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of most active and least active molecules\u003c/h2\u003e \u003cp\u003eThe docking score of all the drug molecules for each target were arranged in ascending order and the frequency of the docking score was analysed. The maximum no. of drugs that fall in a range of docking scores were considered as \u0026ldquo;threshold\u0026rdquo; value and the list of drugs are classified into two different portions. The docking scores which are more than the threshold were considered as active molecules and the docking scores that are less than the threshold was considered as inactive molecules respectively. Further, the top 10 ranked candidates from the active molecule and 10 low-ranking candidates from the inactive molecules were analysed in detail.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of therapeutic indications toward the SARS-CoV2 targets\u003c/h2\u003e \u003cp\u003eThe top 10 and bottom 10 compounds were selected to analyse the most active and least active therapeutic indications for the SARS-CoV2 targets. The therapeutic indication of each compound was retrieved from the drug databases and then the distribution of therapeutic indications toward the SARS-CoV2 targets was carried out for the top 10 and bottom 10 compounds. In addition, the reported FDA approved drugs and drugs under clinical trials for COIVD-19 disease were identified among the top scored drugs in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eVirtual screening of drug molecules against 24 targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVirtual screening of the known drug molecules obtained from DrugBank [44] and DrugCentral database [45] was carried out against the 24 SARS-CoV2 viral targets. The molecules were distributed based on the docking score and a bar graph was plotted for each target to identify the number of drugs falling under different docking scores (\u003cstrong\u003eFig. S2 a-b\u003c/strong\u003e). It can be observed that the average maximum docking score for each protein is around -5 kcal/mol to -7 kcal/mol and the least docking scores are around -1 kcal/mol to -4 kcal/mol. The overall docking score distribution showed that the maximum number of the drug showed good binding affinity towards the selected SARS-CoV2 targets.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAnalysis of drug molecules in the active and inactive dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe docking cut-off was determined for each target (i.e., the score range that has maximum no. of drugs) and the drug molecules which scored higher than the cut-off was considered as most active molecules whereas the molecules whose docking score was lesser than the cut-off were considered as least active molecules. \u003cstrong\u003eTable 2\u003c/strong\u003e shows the grouping of the total compounds into active and least active groups based on the cut-off scores. The top ten molecules from the most active groups and bottom ten compounds from the least active group were selected for further analysis. From the distribution of active and least active drugs for 24 SARS-CoV2 targets, it can be observed that among the total number of drugs, the average percentage of drugs falling under the most active drugs was found to be 53.5% while the average percentage of drugs falling under the least active drugs was found to be 46.5%. The threshold value was selected based on the highest docking score for each protein distributed across the drug databases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 2]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe top 10 drugs showed good binding affinity towards the 24 SARS-CoV2 targets and are listed in \u003cstrong\u003eTable 3-5\u003c/strong\u003e along with their existing therapeutic indication for structural, non-structural and accessory SARS-CoV2 proteins respectively. From the table, it can be observed that among a total number of 240 drugs in the top 10, around 160 drugs were unique and 39 drugs were occurred multiple times in the 24 SARS-CoV2 targets. The most occurring drugs (more than 4 times) across the total 24 targets were dihydroergotamine, ergotamine, midostaurin, temoporfin, tirilazad, venetoclax, paritaprevir, suramin and zorubicin which have therapeutic indications for\u0026nbsp;migraine, immunomodulation, antinematodals, anticancer, antiviral and antibacterial properties (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Several studies have reported that\u0026nbsp;dihydroergotamine and ergotamine are having stable interactions with 3CL\u003csup\u003epro\u003c/sup\u003e and also it has the polpypharmacology efficacy [46].\u0026nbsp;In addition, the drugs\u0026nbsp;tirilazad and venetoclax was reported to show stable binding affinity towards multiple targets for SARS-CoV2 which indicates their polypharmacology property suggesting them as potential candidate for drug repurposing [35, 47].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 3-5] [Fig. 2]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eIdentification of existing approved drugs for COVID-19\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to WHO report (WHO-2019-nCoV-therapeutics-2022) two known drugs namely remdesivir (antiviral compound) and baricitinib (anti-arthritis compound) are approved for treatment of COVID-19. Along with these, several studies have reported a list of drugs (\u003cstrong\u003eTable S2)\u003c/strong\u003e under clinical trials and those used for emergency treatment of COVID-19. In the current study, these known drugs were identified among the drugs interacting with 24 SARS-CoV2 targets. Although, the existing FDA approved drugs and drugs which are under clinical trials for COVID-19 were not found among the top 10 drugs, few of them showed good binding affinity towards the selected SARS-CoV2 targets and have been highlighted along with the top 10 drugs as shown in \u003cstrong\u003eFig. 3 and Fig. 4 (a-b)\u003c/strong\u003e. From the graph it can be observed that the known drugs remdesivir, baricitinib, ritonavir, hydroxychloroquine, lopinavir, favipiravir and dexamethasone having good docking scores due to stable interaction with almost all the 24 SARS-CoV2 targets. The two reported approved drugs for COVID 19, remdesivir and baricitinib was found to be interacting with all the SARS-CoV2 targets except for envelope and OR7a protein. Several studies have reported remdesivir an antiviral drug to inhibit the function of RNA-dependent RNA polymerase (NSP12) with binding score of -5 kcal/mol to -10 kcal/mol [48-51]. From our study it can be compared that remdesivir have a binding score of -7 kcal/mol with NSP12 which confirms the significance of the study. The highest docking score of -8.1 kcal/mol was observed with membrane protein while the lowest docking score of -2.2 kcal/mol with NSP7 and an average binding score of -6.5 kcal/mol across the interacting targets. Similarly, baricitinib an anti-arthritis drug, is reported to interrupt the entry and intracellular assembly of SARS-CoV2 into the host cell and interfere the immune response [52, 53]. Interestingly, from our study it is observed to have\u0026nbsp;a highest of -7.9 kcal/mol with membrane which is involve in humoral response during the SARS-CoV2 and host interaction. Meanwhile, the lowest score of -3.4 kcal/mol was observed for nucleoprotein with an average binding score of -5.92 kcal/mol across the interacting targets some of which are involved in viral entry and immune response. This suggest that some of the existing known drugs for COVID 19 have shown good binding energy along with the top 10 drugs which can be of significant importance for repurposing against multiple SARS-CoV2 targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 3- Fig. 4 (a-b)]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003ePrivileged scaffolds identification and their analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor identifying the privileged scaffolds, a total of 160 unique drugs present in the top 10 drugs across the 24 SARS-CoV2 targets were taken to generate 3 levels of scaffolds. It was observed that from the scaffold tree, level 1, level 2 and level 3 consist of 71, 103 and 123 scaffolds respectively. The identified scaffolds were then compared with the scaffold generated in each level for the existing FDA approved drugs and drugs under clinical trials for COVID-19 disease. From the analysis it was observed that there were 4 common scaffolds (6,7,8,9-Tetrahydro-5H-cyclohepta[c]pyridine, 1-Benzazepine, decalin and leucoline) in level 2 and 2 common scaffolds (4,5,6,7,8,8a,9,10-Octahydro-2(3H)-phenanthrenone, Gona-1,3,5(10)-trien-3-ol) in level 3 of the scaffold tree as shown in \u003cstrong\u003eFig. 5\u003c/strong\u003e. The compounds present in the level 2 and 3 of scaffold tree generated from the unique compounds can be considered as potential candidates for designing drugs to inhibit the effect of SARS-CoV2. The common scaffolds can also serve as starting point for the development of novel SARS-CoV2 inhibitors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 5]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe therapeutic indication of all the molecules was identified through DrugBank and DrugCentral databases and the data was analysed to find the therapeutic indications which can be repurposed against 24 SARS-CoV2 targets. The overall distribution of therapeutic indications of top 10 and bottom 10 drugs across the 24 SARS-CoV2 targets are shown in\u0026nbsp;\u003cstrong\u003eFig. 6\u003c/strong\u003e. From the graph it can be observed that the drugs with high binding energy were found to have therapeutic indications for CNS (central nervous system), immunology, cardiology, infectious diseases and pain management. Whereas, the drugs with low binding energy were found to have therapeutic indications for immunology, vitamin deficiency, CNS, infectious disease and anaesthesiology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Fig. 6]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther, the 24 proteins were categorised into five categories based on their role involved in\u0026nbsp;viral biological process \u003cem\u003eviz.,\u003c/em\u003e cell cycle (Spike, Envelope, Membrane, ORF8), immune response (NSP1, NSP5, NSP15, NSP16, ORF6, ORF7a, ORF9b), replication (NSP3, NSP4, NSP7, NSP8, NSP9, NSP10, NSP12, NSP13, NSP14, Nucleoprotein), cellular degradation (NSP2, NSP6) and pathogenesis (ORF3a). The distribution of potential therapeutic indication of the drugs across the disease area for inhibiting the functions of 24 SARS-CoV2 protein was analysed for top 10 and bottom 10 drugs. It was observed that the top scored drugs for proteins involved in cell cycle showed therapeutic indication for infectious disease and neurology diseases\u0026nbsp;whereas the least scored drugs showed therapeutic indications for oncology and also infectious disease. In case of immune response related proteins, the drugs among the top scored drugs had neurological therapeutic indication and drugs with least score had therapeutic indication for infectious diseases. In case of viral replication processes, the top scored drugs were related to oncology disease area and least scored drugs were related to infectious diseases. The cellular degradation category showed drugs with therapeutic indications for disease areas namely pain among the top, infectious disease among the bottom and for pathogenesis it was observed to be neurology and blood disorder among the top and bottom respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the structural proteins, the envelope protein plays a vital role in the morphogenesis and assembly of the viral particles [54, 55]. The best molecules for this protein were obtained from different types of therapeutic indications namely liver disease, pain management, diabetes, and hypercortisolism whereas the least binding molecules are from bacterial infection, vitamin B12 deficiency, and immune suppressant agents. The function of the membrane protein is similar to envelope proteins and it is involved in the assembly and morphogenesis of the viral particles [56, 57]. The best active molecules for these targets are mainly from asthma, migraine, and inflammation therapeutic indications, and the least binding molecules are showed therapeutic indications for liver disease, anaemia, and melanoma. Spike protein is important in SARS-CoV2 infection and transmission of viral genetic material into the human [58, 59]. Anticancer, anti-inflammatory and antidiabetic molecules were identified as the best-screened molecules, the molecules that are used for bacterial infection, and immunosuppressants are identified as the least active molecules. The nucleoprotein involves in RNA transcription and viral replication [60, 61]. The antiviral and anticancer molecules were identified as the best active molecules and the anti-depressive and liver disease molecules were considered as the least active molecules. A total of 15 not structural proteins were taken for the study where NSP11 was not considered due to its smaller size and lack of binding cavity.\u0026nbsp;The NSP1 is important for host cell modification [62, 63]. The best molecules are mainly from the drugs used for pain, cardiovascular disease, anticancer, and migraine. The drugs used for bacterial infection and vitamin B12 deficiency are identified as the least active molecules. NSP2 plays important role in viral infection [64, 65]. The virtual screening results revealed that the drug molecules for antibacterial, migraine and chronic hepatitis C are identified under the best active molecules and the least active molecules are mainly from the anaesthetic agent, liver disease, and kidney disease. NSP3 is important for viral replication [66, 67]. The molecules for anticancer, and migraines are among the best active molecules and mainly molecules for liver disease, urinary tract infection and melanoma have fallen in the least active molecules. NSP4\u0026nbsp;cleaves the\u0026nbsp;replicase polyprotein at the C-terminus at 11 different sites [68, 69]. The radotinib (anticancer molecule) has been identified as the best active compound along with therapeutic indications for asthma, cardiovascular disease etc. The drugs used for hypertension, muscle dysfunction and cystic fibrosis have been identified as the least active molecules. NSP5 (3CL\u003csup\u003epro\u003c/sup\u003e) recognizes substrates containing the core sequence [70, 71]. Drugs used for bacterial infection, hypertension, cancer is identified as the most active molecules. The drugs that are used for depression, fungal infection is identified as the least active molecules. NSP6 plays a vital role in autophagosome and membrane vesicle formation [72-74]. The best active molecules are mainly from cancer treatment, antibacterial, allergy and the least active molecules are from liver disease, hypertension and melanoma. NSP7, NSP8, and NSP9 proteins are majorly involved in viral infection [75-77]. The anticancer, anti-inflammatory and antiviral molecules are identified as the most active molecules and antibacterial drugs, active vitamin D are identified as the least active molecules. NSP10 is majorly involved in viral transcription [78, 79]. The drugs that are used for Parkinson\u0026rsquo;s disease, hypertension and central nervous system is identified as the most active molecules and drugs that are used for obesity, schizophrenia is screened as the least active molecule. NSP12 helps in viral RNA replication and transcription [80, 81]. Drugs are used for migraines, cancer, and anti-inflammation are identified as the most active molecules. The drugs that are involved in vitamin deficiency, immunosuppressants is identified as the least active molecules. NSP13 with zinc-binding domain in N-terminus is a multi-functional protein [82, 83]. Virtual screening analysis has shown that the anticancer molecules are the most active molecules and the antibacterial molecule was considered as the least active molecule. NSP14 is involved in the unwinding activity of RNA from 5\u0026apos; to 3\u0026apos; polarity [84, 85]. The anticancer molecules with drugs for migraine were identified as the best active molecules and the molecules used for vitamin B12 deficiency, bacterial infection was identified as the least active molecules. NSP15 protein is an endoribonuclease enzyme that cleaves the viral RNA and invades the host immune system [86, 87]. The anticancer, drugs for lung disease and pain-killers were identified as the best active molecules and the molecules that are used as antihypertensive, for cystic fibrosis, and immunosuppressant that are identified as active molecules. NSP16 protein actively participated in viral mRNAs cap methylation [88, 89]. The drugs used for anticancer, central nervous system and pneumonia were among the active molecules and the molecules which are involved in liver disease, antiseptic was considered as the least active molecules. From the accessory proteins selected, the ORF3a protein molecule is involved in controlling up-regulates expression of fibrinogen subunits FGA, FGB, and FGG [90, 91]. The anticancer and antibacterial drugs used for migraine are identified as the most active molecules whereas some molecules that are used for liver disease and ant depressive were identified as the least active molecules. The ORF6 is responsible for multiple viral activities [92, 93]. The drugs used for antibacterial, migraine, anticancer were identified as the best active molecules and the drug molecules that are used as antiseptic and aesthetic were identified as the least active molecules. The ORF7a protein involves in virus replication in cell culture [94, 95]. The most active compounds are pain relievers and anti-diabetic molecules whereas the least active molecules are the drugs for vitamin B12 deficiency. The ORF8 protein is responsible for host-virus interaction [96, 97]. The molecules with therapeutic indications for central nervous system, antiviral, anti-inflammation and anticancer were observed among the most active molecules and the least active molecules were those compounds used for treating cancer. Finally, the ORF9B protein inhibits the host\u0026apos;s innate immune response [98, 99]. The most active compounds are majorly for migraine, antibacterial and anticancer and the least active molecules for liver disease and anti-depressive molecules. According to this study, the majority of the active compounds have anticancer, pain, migraine, and anti-inflammatory therapeutic properties, while the least active compounds have anti-depressive, vitamin deficiency molecules, and antiseptics therapeutic properties. This gives an insight into the group of drugs that will can have therapeutic indications towards SARS-CoV2 targets and can be repurposed for developing potential leads for the treatment of COVID 19. \u0026nbsp;In addition, it also narrows downs the category of drugs which need not be considered for repurposing against SARS-CoV2. This indicates that drug repurposing approach can be an effective approach for targeting the multiple targets for SARS-CoV2.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe current study involves exploring new therapeutic indications of known drugs against SARS-CoV2 targets. Virtual screening was performed to analyse the binding energies of 24 SARS-CoV2 associated proteins and 4193 known drugs. The most active and least active molecules for each target was considered on the basis of docking score. The molecules have high docking scores are considered as the most active molecules and the molecules that have low docking scores are considered as the least active molecules. The most active and least active molecules for each target are categorized on the basis of their therapeutic indications. The results clearly shows that the drugs having anticancer, pain, CNS and anti-inflammatory therapeutic properties have good binding affinity against SARS-CoV2 targets. While anti-depressive, vitamin deficiency molecules, and antiseptics drugs were observed among the drugs with low binding affinity. Through this study, we can note that the drugs among the most active molecules with anticancer, pain, CNS and anti-inflammatory therapeutic indications may be used for targeting the SARS-CoV2 targets through drug repurposing and polypharmacology approach. Hence, the most occurring drugs namely dihydroergotamine, ergotamine, midostaurin, temoporfin, tirilazad, venetoclax, paritaprevir, suramin and zorubicin across the selected targets with therapeutic indications for CNS, immunology, anticancer, antiviral and antibacterial properties may serve to be potential drugs for repurposing against COVID-19 disease. Several studies have found drugs with antiviral, anticancer, and anti-inflammatory properties could be used to for targeting SARS-CoV2 proteins [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Also, the presence of existing approved drugs for COVID-19, namely remdesivir and baricitinib among the drugs interacting with the selected targets, validates the relevance of the study. In addition, the identified common scaffolds generated from the most active molecules may be considered for designing drugs that can effectively disrupt the biological process of SARS-CoV2 thus inhibiting the infection and spread of COVID-19 disease. The study can pave way for effective repurposing for the treatment of disease caused by SARS-CoV2.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declared their consent for publishing this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated from the current study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is conceived by GNS. The calculations have been performed by EJ, HS, LS, KK and NS. All the authors analysed and interpreted the results. All the authors checked the computational results and approved the manuscript as the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGNS thanks DST New Delhi for the J.C Bose fellowship. EJ thanks DST for Inspire Fellowship, KK thanks UGC for JRF Fellowship and HS thanks DBT Centre of Excellence for the fellowship. The DBT is thanked for the funding in the form of Centre of Excellence in Advanced Computation and Data Sciences (No. BT/PR40188/BTIS/137/27/2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug repurposing for SARS-CoV2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVirtual screening approach was applied to screen FDA approved drugs with potential therapeutic indications towards the 24 SAR-CoV2 targets that can be repurposed for treating COVID-19 disease.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCoronavirus Disease Situation Reports (WHO) [Accessed in February, 2022]\u003c/li\u003e\n\u003cli\u003eZhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W. China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020; 382:727-733.\u003c/li\u003e\n\u003cli\u003ePedersen SF, Ho YC. 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Cell Rep. 2021; 34:108761.\u003c/li\u003e\n\u003cli\u003eJiang HW, Zhang HN, Meng QF, Xie J, Li Y, Chen H, Zheng YX, Wang XN, Qi H, Zhang J, Wang PH, Han ZG, Tao SC. SARS-CoV-2 Orf9b suppresses type I interferon responses by targeting TOM70. Cell Mol Immunol. 2020; 17:998-1000.\u003c/li\u003e\n\u003cli\u003eAldea M, Michot JM, Danlos FX, Ribas A, Soria JC. Repurposing of anticancer drugs expands possibilities for antiviral and anti-Inflammatory discovery in COVID-19. Cancer Discov. 2021; 11:1336-1344.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eList of 24 SARS-Cov2 targets along with their PDB ID, resolution, sequence length and function.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSl.no\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePDB ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eName of the target\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eResolution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7K7P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHost cell modulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7EXM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCell degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7LOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 3 (PLpro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral replication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModelled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCleaves the C-terminus of replicase polyprotein at 11 sites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6LU7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 5 (3Clpro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecognizes substrates containing the core sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModelled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInitial induction of autophagosomes from host reticulum endoplasmic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6M5I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral replication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7CYQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral replication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6W4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral replication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6W4H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral transcription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6M71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 12 (RNA-dependent RNA polymerase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReplication and Transcription of the viral\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRNA genome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7NN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 13 (Helicase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMulti-functional protein having a zinc- binding domain in N-terminus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7MC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 14 (Proofreading exoribonuclease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProofreading exoribonuclease for RNA replication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6VWW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 15(Uridylate-specific endoribonuclease)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeaves 2\u0026apos;-3\u0026apos;-cyclic phosphates 5\u0026apos; to the cleaved bond\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6W4H\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-Structural Protein 16 (2\u0026apos;-O-methyltransferase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral mRNAs cap methylation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6M0J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpike glycoprotein (S protein)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eViral fusion peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5X29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEnvelope protein (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVirus morphogenesis and assembly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModelled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMembrane protein (M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVirus morphogenesis and assembly\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6WJI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNucleoprotein (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRNA transcription and viral replication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6XDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOpen Reading Frame 3a protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUp-regulates expression of fibrinogen subunits FGA, FGB and FGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModelled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOpen Reading Frame 6 protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlocks multiple antiviral activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6W37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOpen Reading Frame 7a protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVirus replication in cell culture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7JTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOpen Reading Frame 8 protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHost-virus interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6Z4U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOpen Reading Frame 9b protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInhibits the host innate immune response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Distribution of active and inactive drugs across all the 24 SARS-CoV2 proteins. \u0026nbsp;Total number of drugs that has docking score \u0026gt; threshold value has been considered as active (%) control, whereas the drugs that has docking score \u0026lt; threshold value has been considered as inactive (%).\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"107%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSl.No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInactive (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e\u003cstrong\u003eActive (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal compounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThreshold value (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eEnvelope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e42.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e58.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eMembrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e45.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e54.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNucleoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e42.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e57.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eSpike\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e38.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e61.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e60.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e39.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e44.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e55.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e61.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e38.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e63.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e36.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e43.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e56.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e35.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e64.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e44.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e55.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e48.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e51.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e59.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e40.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e28.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e71.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e41.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e58.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e25.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e74.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e43.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e56.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e49.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e50.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eNSP16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e50.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e49.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eORF3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e40.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e59.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eORF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e34.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e65.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eORF7a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e74.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e25.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eORF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e49.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e50.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.080808080808081%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003eORF9b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.151515151515152%\"\u003e\n \u003cp\u003e48.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.141414141414142%\"\u003e\n \u003cp\u003e51.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e4193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.282828282828284%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eList of potential top 10 FDA approved drugs for repurposing against structural SARS-CoV2 targets along with their known therapeutic indication.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapeutic Indication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003eEnvelope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eAbiraterone Acetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eFlufenamic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eGliclazide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ePalonosetron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eNausea and vomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eTrilostane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eHypercortisolism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eFlorantyrone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eDyskinesia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eNiclosamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eHelminthiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ePerampanel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003ePartial seizure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eTriazolam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eInsomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eBentiromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003ePancreas Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003eMembrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eNilotinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eTirilazad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eAntrafenine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eInflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eZafirlukast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eMidostaurin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eAcetic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eSusceptible infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eConivaptan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ePranlukast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003eSpike\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eTalazoparib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e11 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eNilotinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ePrednisolone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAntiinflamatory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eRuboxistaurin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eEtravirine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eHIV-1 infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003ePiketoprofen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eDanazol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eEndometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eLumacaftor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eCystic fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eRaltegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eHIV infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eRevaprazan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eGastric ulcer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003eNucleoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.16326530612245%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.673469387755105%\"\u003e\n \u003cp\u003eParitaprevir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.734693877551024%\"\u003e\n \u003cp\u003eChronic hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e14 (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eTemoporfin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eSuramin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAfrican trypanosomiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e12 (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eLurbinectedin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eLedipasvir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eAntiviral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eAcetyldigoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eCardiovascular\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e19 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eConivaptan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10.38961038961039%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.857142857142854%\"\u003e\n \u003cp\u003eLanatoside C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.753246753246756%\"\u003e\n \u003cp\u003eCardiovascular\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*The repetitive occurrence of drug molecule for the different targets are represented by alphabetical order.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eList of potential top 10 FDA approved drugs for repurposing against non-structural SARS-CoV2 targets along with their known therapeutic indication.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapeutic Indication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eThebacon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCarbamazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eBipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBenmoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e25 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePiketoprofen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eImatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMethysergide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e25 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePiketoprofen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eLevosimendan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCardiovascular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePlafibride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntimetabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePrenoxdiazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCough suppressants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e32 (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSuramin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAfrican trypanosomiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTeicoplanin Aglycone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e12 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e14 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eGlecaprevir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eChronic hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eZorubicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e30 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eParitaprevir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eChronic hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eDihydroergocornine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eNeurotransmitter Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCetrorelix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHormone Antagonists\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e17 (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMidostaurin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eImmunomodulating agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eScopolamine Butylbromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eGastric spasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eVenetoclax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eNaldemedine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eNafamostat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCystic fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e16 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eZafirlukast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBictegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHIV infection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBagrosin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnti-epileptic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e12 (c)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRebamipide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eRadotinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTalniflumate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ecystic fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eNetarsudil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eOcular hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eExatecan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntineoplastic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePicloxydine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eInfection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTrenbolone\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnabolic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eVibegron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMuscle dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e20 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePranlukast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFlibanserin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSexual disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTasosartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCardiovascular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSiponimod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eRelapsing multiple sclerosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eProgesterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eContraceptive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCefazolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eBacterial infecton\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e32 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eSuramin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAfrican trypanosomiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e66 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTasosartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntihypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e17 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMidostaurin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e21 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTalazoparib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e61 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eExatecan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntineoplastic agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCasopitant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eUrinary incontinence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eDactinomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eLomitapide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eHypercholesterolemia -\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMoxidectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eInfection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eDiphenadione\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntithrombotic agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eAvapritinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eGastrointestinal stromal tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e31 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTemoporfin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e64 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eVibegron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMuscle dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTadalafil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eBenign prostatic hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePhenolsulfonphthalein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eRenal function study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBisdequalinium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eKetotifen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAllergic conjunctivitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eDesloratadine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAllergy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMianserin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003edepressive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eSetiptiline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eDepressive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMecloqualone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eInsomnia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMetapramine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntidepressive Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCyproheptadine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eallergic symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e2 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFlufenamic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMethaqualone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntidepressive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eOxcarbazepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eDepressive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eLorajmine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCardiovascular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e78 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eBisdequalinium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eAlpelisib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eNiraparib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003etumor of ovary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRisdiplam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSpinal muscular atrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFenoverine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eirritable bowel syndrome.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eIrinotecan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCalcipotriol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003epsoriasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e14 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e6 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFlorantyrone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eDyskinesia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePaliperidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e31 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eTemoporfin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e93 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eIrinotecan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e47 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eZorubicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCiclesonide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eallergic rhinitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e91 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRisdiplam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSpinal muscular atrophy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e34 (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eLedipasvir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntiviral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e32 (c)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eSuramin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAfrican trypanosomiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e52 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eVenetoclax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e59 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTalniflumate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnti-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eDihydroergocristine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eProgressive mental decline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eErgometrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePostpartum haemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eUbidecarenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTacrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePerflubron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRasagiline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eParkinson\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTalastine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntihistamine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBarnidipine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eGlasdegib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eLercanidipine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHypertensive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e14 (d)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e12 (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTrabectedin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003etumor of ovary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCarsalam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eanti-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMosapramine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eAmrubicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003elung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBetamethasone Phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnti-inflamation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRimegepant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e17 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMidostaurin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eleukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePirarubicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eImmunomodulating agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e51 (a)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003ePazopanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e52 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eVenetoclax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRacepinefrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHypertensive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e16 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eZafirlukast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e55 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBictegravir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHIV infection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePrednisolone Succinate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eanaphylaxis, asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e12 (e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e57 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRebamipide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTucatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eSteviolbioside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntitubercular agent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e112 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eRimegepant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e117 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTucatinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e17 (d)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eMidostaurin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eLeukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e58 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eRadotinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFosaprepitant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eNausea and vomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eAlatrofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eBacterial infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e27 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eLumacaftor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCystic fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eLonafarnib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHutchinson-Gilford syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eNicomorphine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnalgesic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTacalcitol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePlaque psoriasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e27 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eLumacaftor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eCystic fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFazadinium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eEvocalcet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSecondary hyperparathyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eIvosidenib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e66 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTasosartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAntihypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFluspirilene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eOlodaterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eLung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eArgatroban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eThrombosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eHesperidin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHemorrhoids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eQuercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eKidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eNSP16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.278350515463918%\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"38.144329896907216%\"\u003e\n \u003cp\u003eSonidegib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.927835051546392%\"\u003e\n \u003cp\u003eBasal cell carcinoma of skin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e62 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003ePicloxydine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eInfection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eFlavin Adenine Dinucleotide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003evitamin B2 deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eGitoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eCeftobiprole Medocaril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e47 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eZorubicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e53 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eNaldemedine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eEnantate Benzilic Acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eHormonal deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e13 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eTirilazad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.842105263157896%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.68421052631579%\"\u003e\n \u003cp\u003eBerotralstat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.473684210526315%\"\u003e\n \u003cp\u003eAngioneurotic edema\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*The repetitive occurrence of drug molecule for the different targets are represented by alphabetical order.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e List of potential top 10 FDA approved drugs for repurposing against accessory SARS-CoV2 targets along with their known therapeutic indication.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapeutic Indication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eORF3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e78 (b)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eBisdequalinium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eDutasteride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eBenign prostatic hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e31 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTemoporfin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e13 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTirilazad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e12 (f)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e14 (e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e45 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTeicoplanin Aglycone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e11 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eNilotinib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eRevefenacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eChronic obstructive lung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eAdapalene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eDermatology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eORF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e78 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eBisdequalinium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e13 (c)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTirilazad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e31 (d)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTemoporfin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eVaniprevir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eChronic hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e139 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eAdapalene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eDermatology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eSaquinavir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eHIV infections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e19 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eConivaptan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e14 (f)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e126 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eFluspirilene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e30 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eParitaprevir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eChronic hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eORF7a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eHydroflumethiazide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\n \u003cp\u003eHypertensive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eNaftazone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eHemorrhoids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003ePomalidomide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMultiple myeloma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTramazoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003enasal decongestion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eDezocine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eHuperzine A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eHydromorphone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eNaproxen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003ePhenformin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eType II diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003ePrimidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eEpilepsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eORF 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e13 (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eTirilazad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003ePirenoxine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eCataract\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eVesnarinone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAntiviral agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eMagnesium Orotate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMineral supplements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTropatepine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e37 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eThebacon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eRespiratory system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eEnoxolone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAnti-inflammatory agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e52 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eVenetoclax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTraxanox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eImmunosuppressive Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e47 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eZorubicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eImmunomodulation agent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eORF 9b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.1010101010101%\"\u003e\n \u003cp\u003e30 (c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eParitaprevir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.39393939393939%\"\u003e\n \u003cp\u003eChronic hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e12 (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eErgotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e78 (d)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eBisdequalinium Chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAntibacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eUbrogepant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMigraine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e14 (g)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eDihydroergotamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eMigraine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e109 (a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eMosapramine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e52 (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eVenetoclax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eAnticancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e53 (b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eNaldemedine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eSchizophrenia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eNetupitant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eNausea and vomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.157894736842104%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.526315789473685%\"\u003e\n \u003cp\u003eTelmisartan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"51.31578947368421%\"\u003e\n \u003cp\u003eHypertensive disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*The repetitive occurrence of drug molecule for the different targets are represented by alphabetical order.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV2 targets, drug repurposing, virtual screening, therapeutic indication","lastPublishedDoi":"10.21203/rs.3.rs-2083023/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2083023/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrug repurposing is emerging as a vital approach for identifying known drugs with potential therapeutic indications for COVID-19 disease. This aims to categorize and develop therapeutics by identifying existing approved drugs from drug libraries that can effectively reduce drug development time, cost and safety risk. In the current study, virtual screening of known drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-NSP16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). A total of 4193 approved drugs were screened against these targets using AutoDock Vina. The drugs were classified into active and inactive molecules based on the threshold value of the docking score and the therapeutic indications of top 10 and bottom 10 drugs were analyzed in detail. From the study, it was observed that most of the active drugs have antiviral, antibacterial, anticancer, pain and central nervous system based therapeutic properties. The inactive compounds mainly fall in the categories of anti-depressive, vitamin deficiency molecules, and also antiseptics properties. Overall, the outcome of this study will help in identifying the groups of drugs or scaffold that may have activity against COVID-19 targets.\u003c/p\u003e","manuscriptTitle":"A structure-based drug repurposing approach by considering the twenty four SARS-CoV2 Targets: A consensus scoring approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-28 19:14:49","doi":"10.21203/rs.3.rs-2083023/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6dbe7cd1-7392-43b9-828b-ade27a2beada","owner":[],"postedDate":"September 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-27T22:43:55+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-28 19:14:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2083023","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2083023","identity":"rs-2083023","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}