Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents

Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents Debadash Panigrahi, Dr. Susanta Kumar Sahu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4002518/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 Tuberculosis (TB) has become the biggest threat towards human society due to the rapid rise in resistance of the causative bacteria Mycobacterium tuberculosis (MTB) against the available anti-tubercular drugs. There is an urgent need to design new multi-targeted anti-tubercular agents to overcome the resistance species of MTB through computational design tools. With this aim in the present work, a combination of atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), six-point pharmacophore (AHHRRR), and molecular docking analysis was performed on a series of fifty-eight anti-tubercular agents. The generated QSAR model showed statistically significant correlation co-efficient R 2 , Q 2 , and Pearson r-factor of 0.9521, 0.8589, and 0.8988 respectively indicating good predictive ability. Molecular docking study was performed for the data set of compounds with the two important anti-tubercular target proteins, Enoyl acyl carrier protein reductase (InhA) (PDBID: 2NSD) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) (PDBID: 4FDO). Using the similarity search principle virtual screening was performed on 237 compounds retrieved from the Pubchem database to identify potent multitargeted anti-tubercular agents. The screened compound, MK3 showed the highest docking score of -9.2 and − 8.3 Kj/mol towards both the target proteins InhA and DprE1 were picked for 100ns molecular dynamic simulation study using GROMACS. From the data generated, the compound MK3 showed thermodynamic stability and effective binding within the active binding pocket of both target proteins without much deviation. The result of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and energy gap analysis predicts the molecular reactivity and stability of the identified molecule. Based on the result of the above studies the proposed compound MK3 can be successfully used for the development of a novel multi-targeted anti-tubercular agent with high binding affinity and favourable ADME-T properties. Atom based 3DQSAR Molecular docking InhA inhibitor DprE1 inhibitor ADME-T Molecular Dynamic simulation DFT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Tuberculosis (TB) is one of the oldest, contagious, fatal, and pervasive respiratory infections caused by the gram-positive bacteria Mycobacterium tuberculosis (MTB) [ 1 , 2 ]. In recent years, during the COVID-19 pandemic, TB has re-emerged as a major world health problem that causes severe impairment in patients who need long-term treatment [ 3 ]. The report of the World Health Organization (WHO) on TB suggests that the rate of infection and death trolls increased tremendously during this pandemic due to a significant rise in the frequency of multiple drug-resistant TB (MDR-TB) and extremely drug-resistant TB (XDR-TB) cases because of non-adhere and non-compliance towards the available drugs regimen by the patients[ 4 , 5 ]. However, the emergence and spread of resistance to the currently available chemotherapeutic agents is a growing risk for the population worldwide, with increasingly favorable conditions for the bacteria including the HIV epidemic and other co-morbidities such as type 2 diabetes and low-quality life conditions in underdeveloped and economically backward countries, which indicates an urgent need for the development of drugs with shorter treatment time, simpler regimen, more potency and multi-targeted anti-tubercular agents which can be used against the drug-resistant forms of this disease [ 6 – 8 ]. To achieve this objective, we used a computer-based drug-designing approach having aims to identify potential drug candidates and targets against drug-resistant strains of MTB [ 9 ]. In this present work, we used computational techniques like atom-based three-dimensional quantitative structure active relationship (3D-QSAR), pharmacophore modeling, molecular docking, pharmacokinetic, dynamic, toxicity study, and molecular dynamic simulation study to identify potential multi-targeted drug candidates used to treat drug-resistant tuberculosis [ 10 – 12 ]. In recent decades many promiscuous drug targets for anti-tubercular action were reported but two targets, Enoyl acyl carrier protein reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) are considered as the most clinically reproducible, effective and highly vulnerability targets for treatment against MTB, MDR-TB and XDR-TB [ 13 – 15 ]. In the present work an attempt has been made to identify new and effective antagonist towards these two vital druggable targets for the treatment of TB by computational approach. The NADH-dependent enoyl-ACP reductase (InhA) enzyme, is clinically validated as the target of the frontline anti-TB drug isoniazid (INH) and second line drug ethionamide (ETA), encoded by the gene InhA of MTB [ 14 ]. The enzyme InhA catalyse the biosynthesis of mycolic acid which is the central constituent of mycobacterial cell wall (Fig. 01 ). Mycolic acid biosynthesis follows fatty acid synthase (FAS) pathway which involves two enzymatic system, fatty acid synthase I (FAS I) and fatty acid synthase II (FAS II) [ 16 , 17 ]. In FAS I short chain fatty acids are produces while elongation of these chains takes place by FAS II pathway [ 18 ]. X-ray structure of InhA reveals that each subunits has several α-helices and β-strands which contain NADH binding site. In the final step of FAS II, InhA enzyme is responsible for reduction of double bond in the fatty acyl-ACP (acyl carrier protein) into the saturated fatty acyl-ACP which helps to carried out the final step of the fatty acid elongation process [ 19 , 20 ]. Therefore, compounds that can directly inhibit InhA without any activation disrupts the biosynthesis of mycolic acid in the mycobacterium and ultimately lead to death of the organism [ 21 ]. Hence InhA inhibitors have a very promising opportunity towards the treatment of MTB, MDR-TB and XDR-TB [ 22 ]. Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) has been reported as a potential drug target for the treatment of TB. The heteromeric protein DprE1 is an essential component for growth and survival of mycobacterium (Fig. 01 ). Mycobacterial cell wall is composed of polysaccharide arabinogalactan, which is synthesised through DprE1 enzyme mediated redox reaction. During the reaction the oxidase enzyme DprE1 carried out conversion of decaprenylphosphoryl-d-ribose (DPR) to decaprenylphosphoryl- d-arabinose (DPA) by epimerization via an intermediate decaprenylphosphoryl-2-keto-β-derythro-pentofuranose (DPX) [ 23 – 25 ]. Inhibition of DprE1 disrupts the synthesis of arabinogalactan, weakening the bacterial cell wall and making the bacteria more susceptible towards the chemotherapeutic agents used for the treatment of MTB, MDR-TB and XDR-TB [ 26 ]. The process of development of new molecules using the virtual screening workflow has a crucial significance due to the addition of artificial intelligence (AI) and machine learning (ML) [ 27 ]. Identifying hit molecules through computational drug discovery has proved to be a meaningful methodology in the recent years [ 28 ]. Among the various ways of drug design and discovery, structure based similarity search and screening is a key concept which now has been routinely used in the designing and discovery of new chemotherapy molecules [ 29 ]. Similarity search is based on the concept that the two molecules having structural similarity shares similar properties and biological action [ 30 ]. Thus finding molecules similar to a known active molecule is one of the key towards drug discovery. Drug discovery based on similarity search improve the odds of researchers of finding more active molecules at the lowest cost and with the highest probability of success [ 30 , 31 ]. Now a days involvement of different in silico modules of computer aided drug designing (CADD) like 3D-QSAR, molecular docking, ADME-T prediction and simulation study gain enormous importance and helps in a great deal towards finding of most effective drug compounds for a particular drug target of any disease [ 32 ]. In this regard we have performed the study on all 58, 2-nitroimidazooxazines derivatives anti-tubercular agents through the use of CADD techniques to detect and identify highly effective multi targeted drug candidates that will produce more stable chemical bonding with the two most potential protein targets InhA and DprE1 of mycobacterium for the treatment of tuberculosis. In the first phase of work, we performed atom-based 3D-QSAR and ligand-based pharmacophore hypotheses to identify the features responsible for the biological activity of the data set compounds for anti-tubercular function. Subsequently, molecular docking study was performed for the ligands to establish the intermolecular interaction of ligands towards the amino acid residues at the active site of the two target proteins InhA and DprE1. In the second phase, virtual screening of pubchem database was carried out by taking the best docked compound from the series as reference compound for finding the structurally similar compounds. The selected compounds were then screened by their docking results with the two target proteins InhA and DprE1. Based on docking results, the screened compounds were finally subjected to the study of ADME-Tox and drug likeliness applying the Lipinski rule of five. The work has concluded with molecular dynamic simulation study and density functional theory analysis to investigate the stability and reactivity of the identified ligand within the protein- ligand complex against InhA and DprE1 proteins. 2. Material and Methods 2.1 Data set of ligands A set of 58, 2-nitroimidazooxazines derivatives was taken from the previously published literature for the present study which are sharing same activity and assay procedure with significant variations in their structure and potency [ 33 ]. The observed potencies of the compounds in the data set have IC 50 value ranges from 0.035-2.8 µm which were further converted to pIC50 by using the mathematical formula given as Eq. 01: pIC 50 = − log 10 (IC50) ………………………………Eqn.01 To generate the 3D-QSAR models the dataset of 58 compounds randomly divided into a training set of 41 compounds and test set of 17 compounds as presented in (Table S1 ). Training set of compounds are used to generate the models and validation of the developed models was performed by using test set compounds [ 34 ]. 2.2 Preparation of ligands and alignment Molecules selected for the study were constructed using Chem Sketch of Schrodinger suite and then subjected to geometrical optimization using Ligprep module. After energy minimization low energy 3D structures was obtained for each ligands. Alignment of ligands was done by using flexible ligand alignment option of maestro software [ 34 ]. It is one of the important step in order to generate precise and accurate 3D-QSAR models [ 35 ]. All the data set ligands were aligned in such a manner that they are superimposed on one another which helps in studying and observing variations of the structural entities and their relation with one another (Fig. 02 ). 2.3 Pharmacophore modelling Pharmacophore hypothesis modeling is commonly the spatial arrangement of different chemical features similar to two or more active ligands, which explains the interaction involved in binding ligands with the target protein [ 36 ]. To generate a common pharmacophore hypothesis the ligands of the series were divided into active and inactive according to their activity threshold value [ 37 ]. The activity threshold values were kept at 6 and 5 for active and inactive ligands respectively. The dataset of ligands having pIC 50 distribution ranges from 5.553–7.523 was used for generating pharmacophore model. PHASE module of Schrodinger Maestro software was used to generate pharmacophore model which provides a standard set of six pharmacophoric features like hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), aromatic ring (R), negatively ionisable ( N) and positive ionisable (P) group which affect the ligand-target interaction [ 38 , 39 ]. The hypothesis identified by PHASE generate the models according to the active ligands superimpose on features associated with the hypothesis. A six-point common pharmacophore hypothesis was identified from all the active ligands having identical set of features with very similar spatial arrangement and keeping a minimum intensities distance of 2.0 Å. The best common pharmacophore hypothesis was selected depending on the survival score. The high scoring hypothesis used to create QSAR models. 2.4 Building of QSAR models PHASE modules of software have two types of molecular alignment, first is pharmacophore-based alignment and the second is atom-based alignment [ 36 ]. The pharmacophore-based model fails to explain the features of the ligand and the study of whole molecular structure which is needed for the stearic interaction of the ligand with the target proteins [ 34 ]. In the atom-based QSAR models study of the whole molecular structure of the ligands is carried out hence it is more useful in explaining the structure activity relationships. During the generation of atom-based 3D-QSAR models, the structural features of each atom is treated as van der Waals spheres [ 35 , 40 ]. The atoms are treated as hydrogen bond donor-D (hydrogen bonded to elements like N, O, P, and S), hydrophobic or nonpolar-H (C, Cl, Br, F, I), negative ionic group-N (atoms of negative charge), positive ionic group-P (atoms of positive charge), electron-withdrawing including hydrogen bond acceptor – W (non-ionic atoms like N, O) and miscellaneous- X (other types of atoms) as per simple internal rules [ 41 , 42 ]. During the study, the features of the ligand are mapped to a 3D cubic grid space. Generation of QSAR models was achieved by setting all the parameters in default and the PLS factor as 8. Atom-based 3D-QSAR models are generated by assigning the percentage of ligands 70% and 30% to the training and test sets respectively. The models were developed by considering descriptors as independent variables and biological activity as dependent variables. 2.4.1 Validation of the developed models The developed QSAR models were used to predict the biological activities of new compounds hence to check the robustness of the generated atom-based 3D-QSAR models both internal and external validation was performed [ 43 ]. The data set is divided into training and test sets containing 41 and 17 compounds respectively. Atom-based 3D-QSAR models were generated for the training set of compounds and external validation was performed for the test set of compounds to check its predictiveness. The developed models were validated by considering statistical parameters like squared correlation coefficient (R 2 ), cross-validated correlation coefficient (Q 2 ) for the test set, standard deviation of regression, variance ratio (F), Pearson’s correlation coefficient (Pearson-r) and root mean square error (RMSE), significance level of variance ration (P). The predictive ability of the QSAR models for both training and test set was analysed based on the regression coefficient value (R 2 ) and crossed validation coefficient (Q 2 ) value [ 34 , 35 , 44 ]. 2.5 Molecular docking study The molecular docking simulation study is a computational approach that helps to find ligands that can effectively fit geometrically and energetically into the binding pockets of the target proteins. It also helps to predict the types of energy of interaction between ligands and target proteins [ 45 ]. In the present study, molecular docking was performed by PyRx (Autodock vina) tools version v0.8 programs [ 46 – 48 ]. The docking poses with the least interaction energy was analyzed and visualized by using Discovery Studio Visualizer. 2.5.1 Protein preparation The whole data sets of compounds were docked into the active site of the two most druggable targets of anti-tubercular action, NADH-dependent enoyl-ACP reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1). The X-ray diffraction-based, 3D crystallography structures of InhA and DprE1 having PDB ID 2NSD and 4FDO with good resolution 1.9 and 2.4 Å were retrieved from the RCSB protein data bank ( www.rcsb.org ). Further optimization of the protein structure was done by using Biovia Discovery Studio. The missing hydrogen atoms and residues were added. All the water molecules not involved in binding and co-crystallize ligands were removed and minimization of energy was performed. The final 3D structure of the target proteins was evaluated using Biopredicta modules, The Ramachandran plot obtained (Fig. S1 ) showed more residues presented in the most favored regions indicating the prepared proteins are favourable to carry out molecular docking study. 2.5.2 Protein-Ligand docking The protein-ligand docking study of the chosen protein-ligand complex was performed by using the Virtual Screening software interface PyRx (Autodock vina) tools version v0.8. During docking analyses, protein structures were kept rigid and ligands were kept flexible [ 49 ]. The exhaustiveness was set at 8. After uploading the selected target proteins and ligands into the software, using the algorithm energy minimization was performed with the Universal Force Field (UFF). Then, both ligands and protein structures were saved in ‘.pdbqt’ format using the Open Babel tool present in the software. Around the active binding site grid box was generated. The grid box’s size and coordinates were adjusted by tracking the boundary line of the box. The conformational search algorithm used in PyRx is the Lamarckian genetic algorithm. The docking method used in the present work was semi-flexible docking. After docking, the software displayed the binding energy with different conformers, and it was saved in ‘.csv’ format. The results of docking were split into individual conformers by using Autodock Vina. Then, the docking output files were analyzed for the interactions study between the ligands and the amino acid present at the active site of target proteins using Discovery Studio Visualizer (47). Each conformer and the protein were loaded on Discovery Studio Visualizer and observed the interactions. The best conformer was selected based on the docking score and better non-covalent bond interaction. 2.6 Virtual screening Virtual screening is an in silico, cost-effective as well as high-speed technique consisting of computational analog of the High Throughput Screening (HTS), and is characterized as the computational screening of chemical compounds present in large libraries like ZINC, Pubchem, ChEMBL, ChEBI, etc for bioactive molecules [ 50 , 51 ]. This is enormously beneficial for the researcher to avoid cost-effective experiments testing thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. Different approaches for the virtual screening of compounds are, first the parallel approach, in which both ligand-based and structure-based are run independently and the best candidate compounds selected separately from both are considered for biological evaluation [ 52 ]. Secondly, the hybrid method which comprises the combination of both ligand-based and structure-based techniques into a standalone method involves two approaches (a) interaction-based methods and (b) a combination of molecular similarity and docking techniques [ 53 ]. Third the reverse sequential approach includes structure-based virtual screening followed by 2D similarity searching using the best hit molecule as a reference molecule. In this approach, the first docking of ligands on the target protein was performed to identify the active compound and then explore the libraries of ligands for 2D similarity search with the initial active compound [ 54 ]. In the present work on the basis of docking result of the ligands from the data set with both the target proteins, compound number 56 was selected as the most active hit molecule for performing 2D similarity-based virtual screening and was taken as a reference compound to identify 2D similar ligands from PubChem database applying similarity percentage as 70%. Around 237 ligands were identified based on the similarity search, which was again screened by performing a docking study, drug-likeness, and ADME-Tox study. The docking procedure was validated by re-docking the co-crystal ligand against the respective drug target proteins. 2.7 Pharmacokinetic and Drug likeness Prediction Along with the optimum binding affinities of the lead molecules with the target protein the potency of the hit molecules is another driving factor in the drug development process. To become therapeutically successful and effective the identified hits must possess high biological actions with low toxicity [ 55 ]. The evaluation of ADMET (A: Absorption, D: Distribution, M: Metabolism, E: Excretion, T: Toxicity) properties of small molecules experimentally is high-priced and time-consuming. Therefore, the approach of computational evaluation of pharmacokinetic (PK) and toxicity profiles of small molecules has been effective and an important element in the small molecule evaluation as a drug candidate in the initial stage of drug development [ 56 , 57 ]. Nowadays, the study of ADME-Tox properties has become an essential field of drug discovery which significantly reduces the clinical failure of lead compounds. ADME-Tox prediction for all the ligands selected from virtual screening was made through ADMET lab 2.0 a user-friendly freely available web server ( https://admetmesh.scbdd.com ) [ 24 , 32 ]. The properties assessed during the study are partition coefficient, aqueous solubility, % of oral absorption, plasma protein binding, skin permeability, blood-brain barrier, plasma protein binding, metabolism, and elimination. Additionally, various toxicity aspects such as the maximum tolerated human dosage, hepatotoxicity, skin reactivity, mutagenicity, hERG inhibitor, for drug-likeness analysis number of rotatable bonds, molecular weight, number of hydrogen bond donors, number of hydrogen bond acceptor and topological polar surface area respectively were studied. The lead compounds were further subjected to estimate their drug-like properties by using the Lipinski rule of five [ 58 ]. 2.8 Molecular Dynamic Simulation Molecular dynamics (MD) simulation study plays a decisive and vital role in the identification of potential small compounds for biological drug targets due to their ability to provide detailed interactions of ligand-protein interaction at the atomic level [ 59 ]. MD simulations help the researchers study the conformational changes, binding events, and structural stability of both protein targets and ligands. Molecular dynamics studies bridge the gap between the structural information and the dynamic behavior of target proteins which helps in the rational design of potential drug candidates through a deeper understanding of their binding mechanisms and interactions with the various target proteins [ 60 , 61 ]. The simulations study and generation of trajectory files were performed by GROningen MAchine for Chemical Simulations (GROMAC) software [ 62 ]. The best docking conformations of selected ligands with both the target proteins of PDB ID 2NSD and 4FDO were selected for the MD simulation study. The CHARMM27 force field and simple point charge (SPC) water solvation models were selected for study. A cubic boundary box and the counter ion Na + Cl - of concentration 0.15M were added to neutralize the system. The energy minimization was performed by selecting the steepest descent algorithm as an EM integrator with 5000 steps. Simulation was conducted under the equilibration parameters NPT and NVT at 300k, 1 bar pressure and thermostat relaxation time of 100ps. Leap frog was selected as simulator and 100ns under simulation time were executed using mdrun program in GROMAC. Trajectories files were generate for analysing various dynamic parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), solvent accessible surface area (SASA), binding free energy estimate (MM-PBSA) and H-bonds [ 63 , 64 ]. 2.9 Density functional theory (DFT) analysis The density functional theory ( DFT) analysis was carried out to analyze the electronic properties of best identified hit obtained from virtual screening [ 65 ]. Gaussian 09 software tool has been used to perform geometry optimization and total energy calculations by using the Becke-3-Lee-Yang-Parr (B3LYP) function with the standard 6-311 + + G (d,p) basis set. Visualization of the structure and the analysis of the outputs were carried out with Gauss-View software [ 66 ]. Frontier Molecular Orbital (FMO) studies can predict the chemical reactivity of compounds and identify their stability. The calculated energies, the energy of the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and the gap between them were calculated to study the chemical stability of the identified molecule [ 67 ]. The chemical potential (µ), chemical hardness (η), chemical softness (S), and global electrophilicity (ω) were calculated for the newly screened inhibitor. Mathematically, µ index was derived according to the frontier molecular orbital LUMO and HOMO by using the Eq. (2). The chemical hardness (η), chemical softness (S), and global electrophilicity (ω) were computed using the expressions (3), (4) and (5) respectively [ 68 , 69 ]. µ = (E HOMO +E LUMO ) / 2 (2) η = E LUMO - E HOMO (3) S = 1 / η (4) ω = µ2 / 2η (5) 3 Result and Discussion 3.1 Pharmacophore model design During pharmacophore model generation the data set of 58 compounds was divided into active and inactive sets of compounds. The PHASE module of the Schrodinger software was used to generate six features (A, D, H, R, N, P) based on 3D pharmacophoric models. The developed models help to predict the biological activity by prognosis of the features necessary for binding of ligands towards target protein. By using twenty-two active compounds from “pharmaset” we are generating the models having common pharmacophoric features to these active sets of compounds. The scoring and ranking of generated pharmacophoric models were done to identify the best hypothesis. The configuration of site points, the magnitude of vectors, selectivity, and activity with overall energies were considered for the scoring algorithm. Six point pharmacophore hypothesis which included features, one H-bond acceptor (A), two hydrophobic groups (HH) and three aromatic rings (RRR) denoted as AHHRRR is selected as the finest hypothesis based on its scoring (Table. 01). The top pharmacophore model with good predictive power for both active and inactive ligands was associated with the six-point hypothesis AHHRRR. Further, the predictability of a well pharmacophoric hypothesis was confirmed by considering its survival score and adjusted score. As the hypothesis, AHHRRR_1 has the highest survival score (5.222) and adjusted score (3.81) considered the best hypothesis for predicting the structural features required by both active and inactive ligands towards binding with its target protein to perform therapeutic action. The image of distances and angles between the pharmacophoric sites (Fig. 3 a) and hypothesis images for active ligand (compound no. 03) and inactive ligand (compound no. 18) are represented in Fig. 3 b and 3 c respectively. The pharmacophoric features (A) hydrogen bond acceptor mapped on etheric ‘O’ atom present between imidazooxazine and methyl biphenyl ring, the two hydrophobic group (HH), first mapped on -CF3 group attached to 4th position of benzene ring and second is on oxazine ring of fused imidazooxazine ring. Among three aromatic rings (RRR) features, the first was present on the imidazole ring of the bicyclo imidazooxazine ring, and the second and third were present on biphenyl rings attached to imidazooxazine ring. The hypothesis reveals that the identified pharmacophoric features are essential for effective binding of ligands with the target proteins for showing anti-tubercular action.. 3.2 Generation of atom based 3D-QSAR model The atom-based 3D –QSAR study was used to generate models relying on the alignment of the ligands in 3-dimensional space. The data set of 2-nitroimidazooxazines containing 58 compounds was divided into 41 training sets and 17 test set molecules randomly. The atom-based 3D-QSAR models were developed by using PHASE modules of Schrodinger software. The advantage of the PHASE algorithm is to get a 3D-contour map based on favorable and unfavorable regions. In the present study, the atom-based QSAR models were developed for training sets by considering partial least square (PLS) factor 8 and further validated by using test set compounds. 3.2.1 Analysis of developed QSAR models The predictivity of the developed atom-based 3D-QSAR models with eight PLS factors was validated internally and externally for both training and test set compounds. The statistical parameters, squared correlation coefficient (R 2 ), cross-validated correlation coefficient (Q 2 ), standard deviation of regression, variance ratio (F), Pearson’s correlation coefficient (Pearson-r), and root mean square error (RMSE), significance level of variance ratio (P) were used to evaluate the quality of the QSAR models. The summary of the statistical data of all the developed atom based QSAR models are listed in Table. 02. The PLS factor 8 model has the lowest standard deviation (SD) 0.1424 and the values of squared correlation coefficient (R 2 ) for the training set and cross-validated correlation coefficient (Q 2 ) for the test set compounds are 0.9525 and 0.8589 respectively indicates robustness in predictivity of the developed model for test set of compounds. Higher value of F (80.2), Pearson-r (0.8988) and other statistical parameters were also within the acceptance range implies that the built QSAR model is having good precision and used for further analysis and study. The linear scattered plots of actual versus predicted pIC 50 for training and test set are given in Fig. 4 a and 4 b indicates the predictive ability of the generated QSAR model. The QSAR model was developed on the basis of features of the atoms attached to the core ring system such as hydrophobicity or non-polarity, positive ionic interaction, negative ionic interaction, electron-withdrawing effect, and other interactions. The result of atom type fraction contribution towards the developed atoms-based 3D- QSAR models is tabulated in Table. 03. The result of atom type fraction contribution reveals that the presence of hydrophobic or non-polar substitutions and electron withdrawing group plays a significant and important role towards the anti-tubercular activity whereas the presence of positive and negative ionic interaction groups has a mild role towards anti-TB activity. During visualization of the developed atom-based 3D QSAR models in PHASE and study the correlation of activity with various atomic contributions was performed as colored cubes for both training and test set compounds. The developed QSAR models allowed us to find different atomic contributions like the presence of hydrophobic or non-polar groups, electron-withdrawing groups, and positive and negative ionic groups towards anti-tubercular activity. This method used atom types and their occupancy position in a grid of cubes for predicting properties and to visualize the regions that are favorable and unfavorable of the anti-tubercular activity. The maps generated for different atomic contributions in atom-based 3D QSAR are shown in Figs. 5 a-d for a training set compound (18) and Figs. 6 a-d for a test set compound (41). In these contour pictorial presentations of hydrophobic or non-polar interaction magenta colour cube shown is unfavourable and the green colour cube is favourable, for negative and positive ionic interaction yellow cube contribute positively while the red and purple cube contributes negatively. Lastly, for the electron-withdrawing map, the green color cube is favorable and the red color is unfavorable for the bioactivity of the ligand. The contribution map generated for the atom-based 3D-QSAR study indicates the required structural features for the interaction of ligands with its target protein. These maps further allow us to diagnose the particular atoms or groups attached to the core ring system that craves a particular physiochemical property to augment the anti-tubercular activity of ligands. 3.3 Molecular Docking Study All the 58 compounds of the data set were docked into the binding pockets of the two most effective and potential drug target proteins for anti-tubercular action, NADH-dependent enoyl-ACP reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) having PDB ID 2NSD and 4FDO respectively by PyRx tools version v0.8 using Autodock vina. The drug-binding scores in the form of kcal/mol for all the compounds are mentioned in TableS2. Afterward, compound 56 with the highest interaction energy of -8.2 kcal/mol and − 9.6 kcal/mol towards both the target proteins InhA and DprE1 was selected for analysis and used for retrieving the compounds having structural similarity up to 70% from the PubChem database. The selected compounds from the database were further screened by using docking, ADME analysis, and molecular simulations (MD) study. 3.3.1 Structural similarity based Virtual Screening To find the effective multi-targeted anti-tubercular agent, compounds from the PubChem database were screened based on structure similarity by taking compound no 56 as a reference compound. About 237 ligands were identified and their structure was retrieved from the database for further screening. These compounds were then docked into the grid pockets of both target proteins having PDB ID 2NSD and 4FDO. Based on the significant docking scores of > − 6.5 kcal/mol for both the targets only 09 compounds were selected for screening of their drug-like properties and ADMET predictions. For validation of the docking study, the co-crystallized ligand of the receptor was extracted and re-docked into the binding pockets of the respective target proteins. The result of the docking study of the top-ranked compounds has been reported in Table. 04. Post-docking analysis of the screened compounds reveals that compound CHEMBL566642 (MK3) showed the highest docking score of -9.2 and − 8.3 kcal/mol into the binding pockets of both the selected druggable targets for anti-tubercular activity. The 2D- 2D-dimensional docking interaction result of reference compound (56), screened compound (MK3), and co-crystallize ligands for both the receptors was reported in Table. 05. Upon examining the docking features of the identified hit (MK3) with target proteins InhA (2NSD) it was found that it has formed one H-bond with Ile194, П- П stacked interaction with Phe149, Ile194, and П-alkyl interaction with Ala157 and Ile215 residues at the active site of protein (Fig. 7 b) and form two H-bond with Asn135, Asn144, carbon-hydrogen bond with Thr225, Glu190, Gly140, П- П stacked interaction with His415, П-alkyl interaction with Tyr226 and Ala139 residues present at active site of target protein DprE1(4FDO) (Fig. 7 e). 3.4 In silico Drug likeness and Pharmacokinetic (ADME-T) analysis After the structural similarity virtual screening, the best nine identified hits were utilized for drug-likeness and ADME-T predictions using the open web server ADME-T lab 2.0. The property of drug-likeness will be analyzed using the Lipinski rule of five violations. Further, other ADME-T properties like water solubility, pharmacokinetics, and toxicity of the ligands were analyzed. Drug-likeness studies qualitatively measure the chance of a molecule to turn into an oral drug concerning its bioavailability. The drug-likeness and Rule of Five prediction properties for the top nine compounds were summarized in Table. 06. The results exhibited that the nine screened compounds showed good drug-likeness with zero violation of rules. The acceptance value for all the parameters, molecular weight (≤ 500), LogP (≤ 5), number of hydrogen bond acceptors (0–12), hydrogen bond donors (< 05), number of rotatable bonds (0–11) and topological polar surface area (< 140Å2) showed significance oral bioavailability because of high membrane permeability. All the compounds having excellent synthetic accessibility score less than 06 to quantify the complexity of the molecular structure and ring system and can be synthesized easily. Advanced knowledge of pharmacokinetic and toxicity study results is helpful in the design of potential drug candidates with less toxicity. All the screened compounds were evaluated for their drug-like behavior through analysis of pharmacokinetic properties and toxicity study. The results are listed in Table. 07. For all the identified compounds, the LogS value is within the range of -4.5–0.5 log mol/ltr indicating good aqueous solubility which is important for the estimation of absorption and distribution of drug within the body. The predicted value of plasma protein binding (PPB) and blood-brain barrier (BBB) comes within the acceptance range of 80–90% and 0.0-0.3 for all the screened compounds respectively. The predicted value for human hepatotoxicity (H-HT) and drug-induced liver injury (DILI) are within the acceptance range revealing that the compounds caused hepatotoxicity in high doses. The acceptance value of the result for the Ames mutagenicity and skin sensitization study indicates that these compounds are safe from carcinogenicity and inflammatory skin reactions. All pharmacokinetic properties results fit well within the acceptance range defined for use in humans and reveal their potential as new multi-targeted anti-tubercular agents. 3.5 Prediction of anti-tuberculosis sensitivity Further, the screened compounds were investigated to predict their minimum inhibitory concentration (MIC) against eight different Mycobacterium species by employing an online mycoCSM server [ 70 ]. Only Mycobacterium tuberculosis (MTB) MIC values were extracted and analyzed with marketed standards (Isoniazid and Rifampicin). The predicted MIC values calculated by mycoCSM are depicted in Table 08 . The result indicates that the MIC value of the hit molecule MK3 (-6.181µM) was close to the MIC value of rifampicin and higher than that of isoniazid. Lower MIC value indicates that less amount of this compound is required to inhibit the growth phase of the organisms, hence this compound may be selected as a potential anti-tubercular agent for further study. 3.6 Molecular Dynamic Simulation study MD simulation study is a computational technique that informs alternation in structure and behavior of protein occur throughout the simulation period. MD simulation also be helpful in the study of protein dynamics, folding, stability, and interaction of protein with ligands. In the present study, MD simulations were performed to verify the stability of the InhA-MK3 and DprE1-MK3 complex in physiological environments, which could not be achieved via molecular docking. Depending on scores of molecular docking the best screened compound (MK3) was selected for MD simulations analysis along with the reference compound (56) and co-crystallize ligands. Using Gromac software of 100 ns period, MD simulations were run by taking the best dock poses of the hit molecule with target proteins. The stability of the binding complex of MK3 with both the target proteins InhA and DprE1 was estimated by evaluating the plots of RMSD, RMSF, RoG, SASA, H-bonds, and binding free energy estimate (MM-PBSA). 3.6.1 RMSD (Root Mean Square Deviation) RMSD measured the average distance between atom locations in the simulated structure and the initial reference structure which is an indicator of how far the molecular dynamics (MD) simulated structure has deviated from its initial configuration. A system with a lower RMSD value has less structural drift and is therefore more stable. The merge RMSD plot of reference compound (56), identified compound (MK3) and co-crystallize ligand with InhA protein (Fig. 8 a) demonstrated stability in complex form. The complex of InhA- 56 became stable in the beginning and became unstable from 30-50ns and further stable from 70ns till the end. In the complex of InhA- MK3 steady confirmations were observed in the beginning followed by unsteady confirmations from 30- 65ns and again become linear till 100ns. Whereas in the complex of InhA- cocrystallize ligand showed variation in beginning and having steady RMSD value from 30-100ns. The RMSD plot for the DprE1 protein with the reference compound (56), identified compound (MK3), and co-crystallize ligand is given in Fig. 8 b indicates no significant deviation in comparison to the unbound protein. The plot for all three compounds shows stability across the whole simulation time of 100ns. Therefore over the course of 100 ns simulation, the identified compound MK3 had a highly steady RMSD of 1.5, demonstrating that it maintained a constant binding mode. Hence it has a promising option for further development and improvement as a potent anti-tubercular agent. 3.6.2 RMSF (Root Mean Square Fluctuation) During MD simulation, Root-mean-square fluctuation (RMSF) was assessed to analyze the impact of lead compounds binding on the flexible portion of the targeted protein. RMSF result also estimates each residue's variations around its average location. Higher RMSF values indicate that the residues are more flexible. Finding flexible protein regions and stiff protein regions can be done with the help of RMSF. The identified hit molecule MK3 interacts with both InhA and DprE1 proteins showing stability in the residues during the simulation study. The RMSF analysis shown in Fig. 9 a and 9 b, the identified hit demonstrating more stability than the reference compound (56) and co-crystallize ligand complex with target proteins InhA and DprE1 respectively. However several residues such as Arg45, Phe109, Arg153 Ile202, Gly205, Trp249 and Leu269 for InhA complex and Thr8, Arg41, Phe267, Arg304, pro329, Phe362, Arg372 and Lys398 for DprE1 complex are highly flexible showed significant RMSF. This RMSF analysis indicates that the hit molecule has substantial stability in comparison to 56 and co-crystallize ligands for both target proteins. The presence of significant RMSF residues outside the active site of target proteins suggests that any conformational changes undergone may not have a substantial impact on the binding ability of MK3 into the active site of the target proteins of InhA and DprE1. 3.6.3 RoG (Radius of Gyration) The Radius of Gyration (RoG) measures the dispersion of a protein’s mass around its center of mass, which helps to identify the expansion and compactness of protein structure. A folded structure or compact structure of the protein is identified if RoG is reduced and the unfolded structure of the protein is indicated in an increase in RoG value. Complexes of both target protein InhA and DprE1 with reference compound (56), identified compound (MK3), and co-crystallize ligand were analyzed for RoG and their results were given as Fig. 10 a and 10 b respectively. The RoG value was lower for MK3 for both complexes with InhA and DprE1 in comparison to 56 and co-crystallize ligands suggesting the structure of InhA- MK3 and DprE1- MK3 is more compact in comparison to other structures of InhA and DprE1 with 56 and co-crystallize ligand. This RoG study result indicates that MK3 structural stability during its interaction with both target proteins demonstrates its tendency to maintain a compact structure during simulation suggesting good RoG stability. 3.6.4 SASA (Solvent Accessible Surface Area) The surface area of protein that is accessible to the solvent is referred to as its "solvent accessible surface area" (SASA). Changes in SASA may be a sign of protein-ligand interactions, folding, ligand binding, or conformational changes. SASA is frequently used to examine the dynamics and stability of proteins. The complex of reference compound (56), identified compound (MK3), and co-crystallize ligand with InhA and DprE1 proteins were used for SASA analysis (Fig. 11 a and b). The study showed a very slight deviation during the simulation due to minor structural changes during complex formation. The complex of MK3 with InhA and DprE1 confirms that this compound revealed acceptable stability. The results from the RMSD, RMSF, and RoG studies were further supported by the SASA measurements, which provided additional information on the stability of MK3 in interaction with InhA and DprE1 target proteins for anti-tubercular action. 3.6.5 H-bonds Analysis (Hydrogen Bonds Analysis) H-bond analysis during MD simulation illustrates the stability of the protein-ligand complex. The H-bond analysis for the complex of reference compound (56), identified hit (MK3), and co-crystallize ligand with target protein InhA and DprE1 were given in Fig. 12 a and 12 b. The H-bonding interaction plot describes that the screened compound MK3 forms an average of 2–4 and 2–5, H-bond within the target site of InhA and DprE1 protein respectively during the simulation study. From the graph, it is evident that the H-bonds formed between the ligand and amino acids of the target protein were conserved during the 100ns simulation for both complexes. In conclusion, the H-bond analysis supported the results of previous structural investigations by providing further evidence of the stability of MK3 in contact with both InhA and DprE1 proteins. 3.6.6 Binding free energy calculation (MM-PBSA) Binding free energy calculation enables the energetic stability of the protein-ligand complex to be assessed, and the binding strength between them to be predicted. The gmx_mmpbsa tool of GROMACS is used for the calculation of the binding free energy of the ligand-protein complex. This gmx_mmpbsa applies the Molecular Mechanic- Poisson-Boltzmann Surface Area (MM-PBSA) method for binding energy calculation. The binding free energy was calculated for the docked complexes of target proteins InhA and DprE1 with compounds 56, MK3 and co-crystallize ligands by using expression 6; ΔG bind = G complex - (G protein + G ligand ) (6) Where G complex is the energy of the protein-ligand complex, G protein and G ligand are the energy of protein and ligand in aqueous solvent, respectively. Other energies like van der Waals energy (E vdw ), electrostatic energy (E elec ), polar solvation energy (G polar ), non-polar solvation energy (G nonpolar ) were also calculated and shown in Table.09. Binding free energy calculation indicates that E vdw , E elec, and G non-polar have a major contribution to the total binding energy because of their negative values but G polar has no contribution because of its positive value. Compound MK3 is found to have a very good binding energy of -42.27 kJ/mol and − 32.39 kJ/mol with the target proteins InhA and DprE1 respectively which suggest that this compound has strong and effective interaction within the active binding pockets of these proteins. All taken together, these results of the simulation study suggest that the screened ligand MK3 has a strong affinity and energy stability, which suggests that this could be used as a potential InhA and DprE1 inhibitor. These findings are promising and highlight that the compound MK3 can be used as a potential multi-targeted anti-tubercular agent. 3.7 DFT Analysis The DFT analysis was carried out for the compound MK3 to analyze its electronic properties and chemical reactivity by Gaussian 09 software tool by using the Becke-3-Lee-Yang-Parr (B3LYP) function with the standard 6-311 + + G (d,p) basis set. Visualization of the structure and the analysis of the outputs were carried out with Gauss-View software. FMO analysis of the identified compound was performed by measuring the energy of the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and the gap between them. Measurement of HOMO and LUMO designate the nucleophilicity and electrophilicity nature of the compound. The reactivity of the compound is checked by the energy gap between HOMO and LUMO, the compound having a small energy gap allows the molecule to be more reactive but less stable whereas the compound having a higher difference margin is less reactive but more stable. Along with HOMO and LUMO energy global reactivity indices such as chemical potential (µ), chemical hardness (η), chemical softness (S), and global electrophilicity (ω) were calculated for the newly screened inhibitor MK3 to check its bioactivity. The results of the study are represented in Table. 10. Furthermore, the Frontier Molecular Orbitals (FMOs) of the screened molecule are given in Fig. 13 . The derived values of ΔEgap and global indices indicate significant chemical and bio-reactivity of the identified molecule MK3. 4. Conclusion Tuberculosis is regarded as one of the fatal infections caused by Mycobacterium tuberculosis in humans, which triggers mobility and morbidity throughout the world because of the upbringing of drug resistance cases. With the aim of reducing the duration of treatment against resistant MTB species and cost of developing new drug-candidates, this study integrated various computer-aided drug design (CADD) techniques to design new potent molecules against two potential anti-tubercular drug targets InhA and DprE1 through virtual screening by integrating similar structure-based drug discovery techniques. We performed atom-based 3D-QSAR analysis, ADMET profiling, molecular docking, MD simulation, and DFT analysis to characterize the title molecule and assess its potential use against drug resistant species of MTB. Through a rigorous assessment process, predictive validated atom-based 3D-QSAR and pharmacophore hypothesis models were used to identify molecular descriptors that exert an influence on the improvement of anti-tubercular activity. The proposed compounds MK1-MK9 confirmed the Lipinski rule of five. During the process of molecular docking, we observed that compound MK3 showed the highest binding energy of -9.2 and − 8.3 kcal/mol, indicating it as a lead-like drug. Furthermore, it exhibited the most favorable interaction with the residues in the binding pockets of the target receptors, InhA and DprE1. ADME-T results showed good absorption, no penetration into the brain, and non-toxic for the newly identified molecule. Further, the 100 ns MD simulation results endorses the findings of the molecular docking result and indicates that the compound, MK3 showed stable interactions with effective RMSD, RMSF, RoG, SASA, and H-bond formation within the active pockets of InhA and DprE1 proteins. In MM-PBSA analysis, the compound MK3 has a binding energy of -42.27 kJ/mol and − 32.39 kJ/mol towards InhA and DprE1 proteins, suggesting this molecule has strongest and effective binding within the active pockets of these proteins. Again, the result of DFT analysis suggests that molecule MK3 tends to exhibit more active anti-tubercular action because of having a smaller ΔEgap of 0.14806 between HOMO and LUMO. After validating all the theoretical results, the identified molecule MK3 has a great chance to work as a new inhibitor of the two most druggable targets InhA and DprE1 for treating tuberculosis, and the outcome of this computation study proposed that the compound MK3 can develop as a potent multi targeted anti-tubercular agent in the future. Declarations Acknowledgement Authors express their gratitude to Schrodinger office, Bangalore, India; and SiBIOLEAD, Little Rock, Arkansas, USA, for the help rendered in this study. 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J Chem Inf Model 60:3450–3456. http://dx.doi.org/10.1021/acs.jcim.0c00362 Tables Table 01 Score of multiple Pharmacophore hypothesis AHHRRR HypoID Survival score Selectivity score Inactive score Site score Volume score Number of matches Adjusted score BEDROC score AHHRRR_1 5.222 2.604 1.412 0.729 0.809 12 3.81 0.932 AHHRRR_2 5.219 2.618 1.417 0.717 0.805 12 3.802 0.932 AHHRRR_3 5.203 2.617 1.412 0.696 0.811 12 3.791 0.932 AHHRRR_4 5.173 2.615 1.36 0.684 0.796 12 3.813 0.930 AHHRRR_5 5.173 2.609 1.394 0.679 0.805 12 3.779 0.932 AHHRRR_6 5.165 2.61 1.368 0.667 0.808 12 3.797 0.932 AHHRRR_7 5.158 2.615 1.384 0.7 0.764 12 3.774 0.932 Table 02 Summary of atom based 3D- QSAR results # Factors SD R 2 R 2 CV R 2 Scramble Stability F P RMSE Q 2 Pearson-r 1 0.4181 0.5008 0.2248 0.2497 0.513 39.1 2.29E-07 0.43 0.388 0.6301 2 0.3426 0.6735 0.3876 0.5036 0.9 39.2 5.82E-10 0.46 0.2992 0.6027 3 0.3081 0.7428 0.418 0.6184 0.863 35.6 5.28E-11 0.51 0.141 0.5528 4 0.262 0.819 0.3231 0.745 0.73 40.7 6.82E-13 0.47 0.2627 0.6191 5 0.2365 0.8566 0.3 0.7905 0.636 41.8 8.48E-14 0.47 0.4242 0.6083 6 0.2055 0.8949 0.3056 0.8577 0.533 48.3 3.23E-15 0.47 0.7028 0.6071 7 0.1714 0.9291 0.2671 0.9002 0.814 61.7 3.94E-17 0.45 0.8226 0.7407 8 0.1424 0.9525 0.2514 0.9254 0.913 80.2 5.65E-19 0.68 0.8589 0.8988 Factors: Number of factors in the partial least squares regression model; SD: Standard deviation of the regression; R 2 : Value of R 2 for the regression; R 2 CV: Cross-validated R 2 value, computed from predictions obtained by a leave-N-out approach; R 2 Scramble: Average value of R 2 from a series of models built using scrambled activities; Stability: Stability of the model predictions to changes in the training set composition. This statistic has a maximum value of 1; F: Variance ratio. Large values of F indicate a more statistically significant regression; P: Significance level of variance ratio. Smaller values indicate a greater degree of confidence; RMSE: Root-mean-square error of the test set; Q 2 : Value of Q 2 for the predicted activities of the test set; Pearson-r: Value of Pearson-R for the predicted activities of the test set Table 03 Atom type fraction contribution of atom based 3D- QSAR models # Factors Hydrophobic/non-polar Negative ionic Positive ionic Electron-withdrawing Other 1 0.503 0.056 0.055 0.339 0.026 2 0.511 0.057 0.053 0.337 0.042 3 0.52 0.06 0.052 0.331 0.037 4 0.511 0.061 0.054 0.344 0.03 5 0.507 0.061 0.054 0.352 0.026 6 0.500 0.06 0.054 0.36 0.026 7 0.501 0.057 0.056 0.362 0.024 8 0.533 0.059 0.057 0.362 0.023 Table 4 is available in the Supplementary Files section. Table 05 Docking interactions result of the reference, screened and co-crystallize ligands with amino acid residues of target proteins. Compound PDB ID-2NSD PDB ID-4FDO H-bond П- σ П- П stacked П-alkyl H-bond C-H bond П-alkyl П- П stacked Alkyl 56 Arg173,Glu169, Ser166 Val163 Phe108 Ala154 - Ser228, Lys134,Tyr415,Gly321 Arg58,Val365 - Leu365,Arg58 MK3 Ile194 - Phe149, Ile194 Pro193, Met199, Met161 Asn135,Asn144 Thr225,Glu190,Gly140 Tyr226, Ala139 His145 - Co-crystallize ligand Ile194 Ile21 - Ala157, Ile215 Trp16, Lys418 Gly321,Thr118, Phe320,Trp230, Gly117, Ile131, Pro116,Ala117, Ser59 Val365,Leu363, Leu317 Tyr60 Val121 Table. 06 Predictions of Drug-likeness and Rule of Five for the top screened compounds Compound ID MW Vol LogP nHA nHD TPSA nRot Synth MCE-18 Lipinski MK1 409.1 355.618 2.955 9 0 97.24 6 3.454 78.545 Accepted MK2 409.1 355.618 3.329 9 0 97.24 6 3.413 78.545 Accepted MK3 411.09 343.02 2.018 11 0 123.02 6 3.617 79.2 Accepted MK4 360.1 319.888 1.876 10 0 110.13 5 3.524 69.632 Accepted MK5 410.1 349.319 2.825 10 0 110.13 6 3.59 78.857 Accepted MK6 360.1 319.888 1.311 10 0 110.13 5 3.415 69.632 Accepted MK7 410.1 349.319 2.435 10 0 110.13 6 3.49 78.857 Accepted MK8 342.11 313.82 1.847 10 0 110.13 5 3.481 66.316 Accepted MK9 367.1 336.84 1.047 11 0 133.92 5 3.514 69.3 Accepted MW = Molecular weight (≤ 500), Vol = vander Waal’s volume, LogP = Distribution coefficient (≤ 5) ,nHA = Hydrogen bond acceptor(0–12),nHD = Hydrogen bond donor (≤ 5), TPSA = Topological Polar Surface area(˂140),nROT = Number of rotatable bond (0–11), Synth = Synthetic accessibility Score (1–6 (excellent), > 6 (poor)), MEC-18 = Medicinal chemistry Evaluation 2018 (≥ 45 excellent) Table 07 Pharmacokinetic (ADME) and Toxicity prediction results for the top screened compounds Compound ID LogS Caco-2 HIA PPB BBB H-HT DILI Ames SkinSen LC50 MK1 -4.446 -4.548 0.004 96.33% 0.114 0.97 0.989 0.964 0.309 5.021 MK2 -4.419 -4.531 0.003 96.01% 0.088 0.973 0.99 0.969 0.307 5.304 MK3 -3.954 -4.491 0.01 92.57% 0.116 0.97 0.995 0.982 0.579 4.51 MK4 -3.398 -4.474 0.004 87.78% 0.146 0.98 0.993 0.985 0.476 4.464 MK5 -4.352 -4.515 0.004 95.68% 0.178 0.97 0.993 0.966 0.387 4.888 MK6 -2.95 -4.464 0.004 82.65% 0.078 0.979 0.99 0.989 0.363 4.578 MK7 -4.286 -4.492 0.005 94.80% 0.09 0.97 0.99 0.977 0.334 5.059 MK8 -3.195 -4.496 0.004 85.68% 0.165 0.971 0.994 0.989 0.555 4.321 MK9 -3.858 -4.526 0.006 81.82% 0.074 0.986 0.991 0.991 0.398 4.518 LogS = Logarithm of aqueous solubility (-4.5 to 0.5 log mol/L), Caco-2 = human colon adenocarcinoma cell lines permeability ( \(>-5.15\text{l}\text{o}\text{g} \text{c}\text{m}/\text{s}.\) ), HIA = Human intestinal absorption (0-0.3: excellent), PPB = Plasma protein binding ( > 80%), BBB = Blood brain barrier penetration (0-0.3: excellent ; 0.3–0.7: medium ; 0.7-1.0: poor ), H-HT = The human hepatotoxicity (0–1), DILI = Drug-induced liver injury (0–1), Ames = The Ames test for mutagenicity(0–1),Skinsen = Skin sensitization (0–1), LC50 = Lethal concentration cause death after 96 hours Table 08 Anti-TB activity prediction of screened hits through online server mycoCSM Compound Predicted MTB. MIC (log µM) MK1 -6.128 MK2 -5.283 MK3 -6.181 MK4 -5.682 MK5 -6.072 MK6 -5.498 MK7 -6.010 MK8 -5.551 MK9 -4.453 Isoniazid -4.942 Rifampicin -6.130 Table 09 MM-PBSA calculations for the complex of DprE1 and InhA protein with compound 56, MK3 and co-crystallize ligands . Compound Complex of InhA with ligands Complex of DprE1 with ligands E vdw (kJ/mol) E elec (kJ/mol) G polar (kJ/mol) G non−polar (kJ/mol) △G bind (kJ/mol) E vdw (kJ/mol) E elec (kJ/mol) G polar (kJ/mol) G non−polar (kJ/mol) △G bind (kJ/mol) 56 -36.58 -0.70 11.80 -3.40 -28.89 ± 4.84 -40.70 -3.12 25.58 -3.66 -21.91 ± 4.60 MK3 -52.74 -2.72 16.48 -3.29 -42.27 ± 2.76 -50.37 -5.34 27.67 -4.36 -32.39 ± 4.02 Co-crystallize ligand -42.18 -0.11 11.39 -3.59 -34.49 ± 5.86 -37.52 -3.69 18.60 -3.11 -25.72 ± 3.20 Table.10 Global indices of the screened compound CHEMBL566642 . Compound Global Indices HOMO (ev) LUMO (ev) Δ E gap (ev) µ (ev) η (ev) S (ev) ω (ev) MK3 -0.26241 -0.11435 0.14806 -0.18838 0.14806 6.754 0.11981 Additional Declarations No competing interests reported. Supplementary Files Supplementryfile.docx Table4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-4002518","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275798251,"identity":"aa1ed301-feb7-42f2-87b2-ba3c7ebb4503","order_by":0,"name":"Debadash 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2","display":"","copyAsset":false,"role":"figure","size":182467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlignment structure of ligands\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/15de0d729ba396378783d08f.png"},{"id":52002398,"identity":"e445a265-b7f9-4053-8f3f-67e5fae220c3","added_by":"auto","created_at":"2024-03-05 08:19:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea Common Pharmacophoric hypothesis (AHHRRR_1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: The Pharmacophoric feature A: H-bond acceptor; appear as light pink sphere with two arrows, H: hydrophobic group; appear as green spheres, R: aromatic rings appear as orange torus in the plane of the ring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb Pharmacophoric 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pIC\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e for test set\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/cb579ae9cf39bc022a04ebec.png"},{"id":52002403,"identity":"ffc8ac31-0dbf-4f11-bfb0-51fa380c34d4","added_by":"auto","created_at":"2024-03-05 08:19:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":475919,"visible":true,"origin":"","legend":"\u003cp\u003ea-d Atom based 3D QSAR visualization map of various atomic contribution for a training set compound: (a) Hydrophobic or non-polar (b) Negative ionic (c) Positive ionic (d) Electron 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7","display":"","copyAsset":false,"role":"figure","size":197210,"visible":true,"origin":"","legend":"\u003cp\u003e(a-f) \u003cstrong\u003eDocking interactions with InhA (PDB ID: 2NSD)\u003c/strong\u003e (a) Docking interaction diagram of Reference ligand (56) (b) Docking interaction diagram of Identified hit (MK3) (c) Docking interaction diagram of Co-crystallize ligand; \u003cstrong\u003eDocking interactions with DprE1 (PDB ID: 4FDO)\u003c/strong\u003e (d) Docking interaction diagram of Reference ligand (56) (e) Docking interaction diagram of Identified hit (MK3) (f) Docking interaction diagram of Co-crystallize ligand.\u003c/p\u003e\n\u003cp\u003e(Notes: H-bond shown as bold green line, light green indicates carbon hydrogen bond, purple colour bond is П- σ interaction, dark pink bond is П- П stacked interaction, light pink indicates П-alkyl interaction with amino acid residues.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/a7fe66a42c10b8f9cb682166.png"},{"id":52002410,"identity":"df7ebdca-4e24-4b7d-88f9-50986cd99bcb","added_by":"auto","created_at":"2024-03-05 08:19:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":85914,"visible":true,"origin":"","legend":"\u003cp\u003ea RMSD trajectory plot of InhA protein (Apo protein) with reference compound (56), identified hit (MK3) and co-crystallize ligand\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eb RMSD trajectory plot of DprE1 protein (Apo protein) with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/48afc329d3cbb396988bc4bb.png"},{"id":52002399,"identity":"b0ef379b-f6b8-40ca-bd22-edec3f7b8865","added_by":"auto","created_at":"2024-03-05 08:19:10","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":107887,"visible":true,"origin":"","legend":"\u003cp\u003ea RMSF plot of InhA protein with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e\n\u003cp\u003eb RMSF plot of DprE1 protein with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/7852b393b0d6426d7967d4e0.png"},{"id":52002406,"identity":"04d59ead-d16d-4bcf-aed8-4969a43457f9","added_by":"auto","created_at":"2024-03-05 08:19:11","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":72876,"visible":true,"origin":"","legend":"\u003cp\u003ea RoG plot of InhA protein with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e\n\u003cp\u003eb RoG plot of DprE1 protein with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/d661c0ebcfcff222b1c530d2.png"},{"id":52002408,"identity":"5082e9b0-4c64-4b0a-a2c8-d3f4fc34878e","added_by":"auto","created_at":"2024-03-05 08:19:11","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":76223,"visible":true,"origin":"","legend":"\u003cp\u003ea SASA plot of InhA protein with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e\n\u003cp\u003eb SASA plot of DprE1 protein with reference compound (56), identified hit (MK3) and co-crystallize ligand.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/ea88d819174849bf4e261fde.png"},{"id":52002834,"identity":"628e4105-9d3c-43dc-8176-cc6184c679cf","added_by":"auto","created_at":"2024-03-05 08:27:10","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":97606,"visible":true,"origin":"","legend":"\u003cp\u003ea H-bond analysis plots for reference compound (56), identified hit (MK3) and co-crystallize ligand with InhA.\u003c/p\u003e\n\u003cp\u003eb H-bond analysis plots for reference compound (56), identified hit (MK3) and co-crystallize ligand with DprE1.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/97fbeab5011718c25ee85095.png"},{"id":52002409,"identity":"10bba140-bd4a-476c-bdc4-7b32d7659b2e","added_by":"auto","created_at":"2024-03-05 08:19:11","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":179380,"visible":true,"origin":"","legend":"\u003cp\u003eThe geometries of the HOMO and LUMO orbitals, along with the value of Δ E\u003csub\u003egap\u003c/sub\u003e of the compound MK3\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/fc0c2912c39bf042e1c06b05.png"},{"id":52041394,"identity":"97fae262-6b91-49cf-98f1-d6d1ac010078","added_by":"auto","created_at":"2024-03-05 18:15:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3256686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/96bef8a4-62c9-4ac2-90d7-303d46d50145.pdf"},{"id":52002401,"identity":"512c6354-8094-4582-8caf-ed6dfd0fcd54","added_by":"auto","created_at":"2024-03-05 08:19:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":841326,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/1614ee920c9605d8af08e5eb.docx"},{"id":52002832,"identity":"02c83c0f-150e-41b8-b107-afce34a1c65e","added_by":"auto","created_at":"2024-03-05 08:27:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":140174,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4002518/v1/bfbef1b47c31c31226338a0d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB) is one of the oldest, contagious, fatal, and pervasive respiratory infections caused by the gram-positive bacteria Mycobacterium tuberculosis (MTB) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, during the COVID-19 pandemic, TB has re-emerged as a major world health problem that causes severe impairment in patients who need long-term treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The report of the World Health Organization (WHO) on TB suggests that the rate of infection and death trolls increased tremendously during this pandemic due to a significant rise in the frequency of multiple drug-resistant TB (MDR-TB) and extremely drug-resistant TB (XDR-TB) cases because of non-adhere and non-compliance towards the available drugs regimen by the patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the emergence and spread of resistance to the currently available chemotherapeutic agents is a growing risk for the population worldwide, with increasingly favorable conditions for the bacteria including the HIV epidemic and other co-morbidities such as type 2 diabetes and low-quality life conditions in underdeveloped and economically backward countries, which indicates an urgent need for the development of drugs with shorter treatment time, simpler regimen, more potency and multi-targeted anti-tubercular agents which can be used against the drug-resistant forms of this disease [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To achieve this objective, we used a computer-based drug-designing approach having aims to identify potential drug candidates and targets against drug-resistant strains of MTB [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this present work, we used computational techniques like atom-based three-dimensional quantitative structure active relationship (3D-QSAR), pharmacophore modeling, molecular docking, pharmacokinetic, dynamic, toxicity study, and molecular dynamic simulation study to identify potential multi-targeted drug candidates used to treat drug-resistant tuberculosis [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent decades many promiscuous drug targets for anti-tubercular action were reported but two targets, Enoyl acyl carrier protein reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) are considered as the most clinically reproducible, effective and highly vulnerability targets for treatment against MTB, MDR-TB and XDR-TB [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the present work an attempt has been made to identify new and effective antagonist towards these two vital druggable targets for the treatment of TB by computational approach.\u003c/p\u003e \u003cp\u003eThe NADH-dependent enoyl-ACP reductase (InhA) enzyme, is clinically validated as the target of the frontline anti-TB drug isoniazid (INH) and second line drug ethionamide (ETA), encoded by the gene InhA of MTB [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The enzyme InhA catalyse the biosynthesis of mycolic acid which is the central constituent of mycobacterial cell wall (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003e). Mycolic acid biosynthesis follows fatty acid synthase (FAS) pathway which involves two enzymatic system, fatty acid synthase I (FAS I) and fatty acid synthase II (FAS II) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In FAS I short chain fatty acids are produces while elongation of these chains takes place by FAS II pathway [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. X-ray structure of InhA reveals that each subunits has several α-helices and β-strands which contain NADH binding site. In the final step of FAS II, InhA enzyme is responsible for reduction of double bond in the fatty acyl-ACP (acyl carrier protein) into the saturated fatty acyl-ACP which helps to carried out the final step of the fatty acid elongation process [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, compounds that can directly inhibit InhA without any activation disrupts the biosynthesis of mycolic acid in the mycobacterium and ultimately lead to death of the organism [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Hence InhA inhibitors have a very promising opportunity towards the treatment of MTB, MDR-TB and XDR-TB [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDecaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) has been reported as a potential drug target for the treatment of TB. The heteromeric protein DprE1 is an essential component for growth and survival of mycobacterium (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003e). Mycobacterial cell wall is composed of polysaccharide arabinogalactan, which is synthesised through DprE1 enzyme mediated redox reaction. During the reaction the oxidase enzyme DprE1 carried out conversion of decaprenylphosphoryl-d-ribose (DPR) to decaprenylphosphoryl- d-arabinose (DPA) by epimerization via an intermediate decaprenylphosphoryl-2-keto-β-derythro-pentofuranose (DPX) [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Inhibition of DprE1 disrupts the synthesis of arabinogalactan, weakening the bacterial cell wall and making the bacteria more susceptible towards the chemotherapeutic agents used for the treatment of MTB, MDR-TB and XDR-TB [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe process of development of new molecules using the virtual screening workflow has a crucial significance due to the addition of artificial intelligence (AI) and machine learning (ML) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Identifying hit molecules through computational drug discovery has proved to be a meaningful methodology in the recent years [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Among the various ways of drug design and discovery, structure based similarity search and screening is a key concept which now has been routinely used in the designing and discovery of new chemotherapy molecules [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarity search is based on the concept that the two molecules having structural similarity shares similar properties and biological action [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus finding molecules similar to a known active molecule is one of the key towards drug discovery. Drug discovery based on similarity search improve the odds of researchers of finding more active molecules at the lowest cost and with the highest probability of success [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Now a days involvement of different in silico modules of computer aided drug designing (CADD) like 3D-QSAR, molecular docking, ADME-T prediction and simulation study gain enormous importance and helps in a great deal towards finding of most effective drug compounds for a particular drug target of any disease [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this regard we have performed the study on all 58, 2-nitroimidazooxazines derivatives anti-tubercular agents through the use of CADD techniques to detect and identify highly effective multi targeted drug candidates that will produce more stable chemical bonding with the two most potential protein targets InhA and DprE1 of mycobacterium for the treatment of tuberculosis.\u003c/p\u003e \u003cp\u003eIn the first phase of work, we performed atom-based 3D-QSAR and ligand-based pharmacophore hypotheses to identify the features responsible for the biological activity of the data set compounds for anti-tubercular function. Subsequently, molecular docking study was performed for the ligands to establish the intermolecular interaction of ligands towards the amino acid residues at the active site of the two target proteins InhA and DprE1.\u003c/p\u003e \u003cp\u003eIn the second phase, virtual screening of pubchem database was carried out by taking the best docked compound from the series as reference compound for finding the structurally similar compounds. The selected compounds were then screened by their docking results with the two target proteins InhA and DprE1. Based on docking results, the screened compounds were finally subjected to the study of ADME-Tox and drug likeliness applying the Lipinski rule of five. The work has concluded with molecular dynamic simulation study and density functional theory analysis to investigate the stability and reactivity of the identified ligand within the protein- ligand complex against InhA and DprE1 proteins.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data set of ligands\u003c/h2\u003e \u003cp\u003eA set of 58, 2-nitroimidazooxazines derivatives was taken from the previously published literature for the present study which are sharing same activity and assay procedure with significant variations in their structure and potency [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The observed potencies of the compounds in the data set have IC\u003csup\u003e50\u003c/sup\u003e value ranges from 0.035-2.8 \u0026micro;m which were further converted to pIC50 by using the mathematical formula given as Eq.\u0026nbsp;01:\u003c/p\u003e \u003cp\u003epIC\u003csup\u003e50\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;log 10\u003csup\u003e(IC50)\u003c/sup\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Eqn.01\u003c/p\u003e \u003cp\u003eTo generate the 3D-QSAR models the dataset of 58 compounds randomly divided into a training set of 41 compounds and test set of 17 compounds as presented in (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Training set of compounds are used to generate the models and validation of the developed models was performed by using test set compounds [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Preparation of ligands and alignment\u003c/h2\u003e \u003cp\u003eMolecules selected for the study were constructed using Chem Sketch of Schrodinger suite and then subjected to geometrical optimization using Ligprep module. After energy minimization low energy 3D structures was obtained for each ligands. Alignment of ligands was done by using flexible ligand alignment option of maestro software [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is one of the important step in order to generate precise and accurate 3D-QSAR models [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. All the data set ligands were aligned in such a manner that they are superimposed on one another which helps in studying and observing variations of the structural entities and their relation with one another (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e02\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Pharmacophore modelling\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePharmacophore hypothesis modeling is commonly the spatial arrangement of different chemical features similar to two or more active ligands, which explains the interaction involved in binding ligands with the target protein [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To generate a common pharmacophore hypothesis the ligands of the series were divided into active and inactive according to their activity threshold value [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The activity threshold values were kept at 6 and 5 for active and inactive ligands respectively. The dataset of ligands having pIC\u003csup\u003e50\u003c/sup\u003e distribution ranges from 5.553\u0026ndash;7.523 was used for generating pharmacophore model. PHASE module of Schrodinger Maestro software was used to generate pharmacophore model which provides a standard set of six pharmacophoric features like hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), aromatic ring (R), negatively ionisable ( N) and positive ionisable (P) group which affect the ligand-target interaction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The hypothesis identified by PHASE generate the models according to the active ligands superimpose on features associated with the hypothesis.\u003c/p\u003e \u003cp\u003eA six-point common pharmacophore hypothesis was identified from all the active ligands having identical set of features with very similar spatial arrangement and keeping a minimum intensities distance of 2.0 \u0026Aring;. The best common pharmacophore hypothesis was selected depending on the survival score. The high scoring hypothesis used to create QSAR models.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Building of QSAR models\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePHASE modules of software have two types of molecular alignment, first is pharmacophore-based alignment and the second is atom-based alignment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The pharmacophore-based model fails to explain the features of the ligand and the study of whole molecular structure which is needed for the stearic interaction of the ligand with the target proteins [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the atom-based QSAR models study of the whole molecular structure of the ligands is carried out hence it is more useful in explaining the structure activity relationships. During the generation of atom-based 3D-QSAR models, the structural features of each atom is treated as van der Waals spheres [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The atoms are treated as hydrogen bond donor-D (hydrogen bonded to elements like N, O, P, and S), hydrophobic or nonpolar-H (C, Cl, Br, F, I), negative ionic group-N (atoms of negative charge), positive ionic group-P (atoms of positive charge), electron-withdrawing including hydrogen bond acceptor \u0026ndash; W (non-ionic atoms like N, O) and miscellaneous- X (other types of atoms) as per simple internal rules [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. During the study, the features of the ligand are mapped to a 3D cubic grid space. Generation of QSAR models was achieved by setting all the parameters in default and the PLS factor as 8. Atom-based 3D-QSAR models are generated by assigning the percentage of ligands 70% and 30% to the training and test sets respectively. The models were developed by considering descriptors as independent variables and biological activity as dependent variables.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Validation of the developed models\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe developed QSAR models were used to predict the biological activities of new compounds hence to check the robustness of the generated atom-based 3D-QSAR models both internal and external validation was performed [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The data set is divided into training and test sets containing 41 and 17 compounds respectively. Atom-based 3D-QSAR models were generated for the training set of compounds and external validation was performed for the test set of compounds to check its predictiveness. The developed models were validated by considering statistical parameters like squared correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e), cross-validated correlation coefficient (Q\u003csup\u003e2\u003c/sup\u003e) for the test set, standard deviation of regression, variance ratio (F), Pearson\u0026rsquo;s correlation coefficient (Pearson-r) and root mean square error (RMSE), significance level of variance ration (P). The predictive ability of the QSAR models for both training and test set was analysed based on the regression coefficient value (R\u003csup\u003e2\u003c/sup\u003e) and crossed validation coefficient (Q\u003csup\u003e2\u003c/sup\u003e) value [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Molecular docking study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe molecular docking simulation study is a computational approach that helps to find ligands that can effectively fit geometrically and energetically into the binding pockets of the target proteins. It also helps to predict the types of energy of interaction between ligands and target proteins [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the present study, molecular docking was performed by PyRx (Autodock vina) tools version v0.8 programs [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The docking poses with the least interaction energy was analyzed and visualized by using Discovery Studio Visualizer.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Protein preparation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe whole data sets of compounds were docked into the active site of the two most druggable targets of anti-tubercular action, NADH-dependent enoyl-ACP reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1). The X-ray diffraction-based, 3D crystallography structures of InhA and DprE1 having PDB ID 2NSD and 4FDO with good resolution 1.9 and 2.4 \u0026Aring; were retrieved from the RCSB protein data bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.rcsb.org\" target=\"_blank\"\u003ewww.rcsb.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Further optimization of the protein structure was done by using Biovia Discovery Studio. The missing hydrogen atoms and residues were added. All the water molecules not involved in binding and co-crystallize ligands were removed and minimization of energy was performed. The final 3D structure of the target proteins was evaluated using Biopredicta modules, The Ramachandran plot obtained (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) showed more residues presented in the most favored regions indicating the prepared proteins are favourable to carry out molecular docking study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Protein-Ligand docking\u003c/h2\u003e \u003cp\u003eThe protein-ligand docking study of the chosen protein-ligand complex was performed by using the Virtual Screening software interface PyRx (Autodock vina) tools version v0.8. During docking analyses, protein structures were kept rigid and ligands were kept flexible [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The exhaustiveness was set at 8. After uploading the selected target proteins and ligands into the software, using the algorithm energy minimization was performed with the Universal Force Field (UFF). Then, both ligands and protein structures were saved in \u0026lsquo;.pdbqt\u0026rsquo; format using the Open Babel tool present in the software. Around the active binding site grid box was generated. The grid box\u0026rsquo;s size and coordinates were adjusted by tracking the boundary line of the box. The conformational search algorithm used in PyRx is the Lamarckian genetic algorithm. The docking method used in the present work was semi-flexible docking. After docking, the software displayed the binding energy with different conformers, and it was saved in \u0026lsquo;.csv\u0026rsquo; format. The results of docking were split into individual conformers by using Autodock Vina. Then, the docking output files were analyzed for the interactions study between the ligands and the amino acid present at the active site of target proteins using Discovery Studio Visualizer (47). Each conformer and the protein were loaded on Discovery Studio Visualizer and observed the interactions. The best conformer was selected based on the docking score and better non-covalent bond interaction.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Virtual screening\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVirtual screening is an in silico, cost-effective as well as high-speed technique consisting of computational analog of the High Throughput Screening (HTS), and is characterized as the computational screening of chemical compounds present in large libraries like ZINC, Pubchem, ChEMBL, ChEBI, \u003cem\u003eetc\u003c/em\u003e for bioactive molecules [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This is enormously beneficial for the researcher to avoid cost-effective experiments testing thousands of compounds by reducing the number of candidate molecules to be tested to manageable numbers. Different approaches for the virtual screening of compounds are, first the parallel approach, in which both ligand-based and structure-based are run independently and the best candidate compounds selected separately from both are considered for biological evaluation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Secondly, the hybrid method which comprises the combination of both ligand-based and structure-based techniques into a standalone method involves two approaches (a) interaction-based methods and (b) a combination of molecular similarity and docking techniques [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Third the reverse sequential approach includes structure-based virtual screening followed by 2D similarity searching using the best hit molecule as a reference molecule. In this approach, the first docking of ligands on the target protein was performed to identify the active compound and then explore the libraries of ligands for 2D similarity search with the initial active compound [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present work on the basis of docking result of the ligands from the data set with both the target proteins, compound number 56 was selected as the most active hit molecule for performing 2D similarity-based virtual screening and was taken as a reference compound to identify 2D similar ligands from PubChem database applying similarity percentage as 70%. Around 237 ligands were identified based on the similarity search, which was again screened by performing a docking study, drug-likeness, and ADME-Tox study. The docking procedure was validated by re-docking the co-crystal ligand against the respective drug target proteins.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Pharmacokinetic and Drug likeness Prediction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAlong with the optimum binding affinities of the lead molecules with the target protein the potency of the hit molecules is another driving factor in the drug development process. To become therapeutically successful and effective the identified hits must possess high biological actions with low toxicity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The evaluation of ADMET (A: Absorption, D: Distribution, M: Metabolism, E: Excretion, T: Toxicity) properties of small molecules experimentally is high-priced and time-consuming. Therefore, the approach of computational evaluation of pharmacokinetic (PK) and toxicity profiles of small molecules has been effective and an important element in the small molecule evaluation as a drug candidate in the initial stage of drug development [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Nowadays, the study of ADME-Tox properties has become an essential field of drug discovery which significantly reduces the clinical failure of lead compounds. ADME-Tox prediction for all the ligands selected from virtual screening was made through ADMET lab 2.0 a user-friendly freely available web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com\u003c/span\u003e\u003cspan address=\"https://admetmesh.scbdd.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The properties assessed during the study are partition coefficient, aqueous solubility, % of oral absorption, plasma protein binding, skin permeability, blood-brain barrier, plasma protein binding, metabolism, and elimination. Additionally, various toxicity aspects such as the maximum tolerated human dosage, hepatotoxicity, skin reactivity, mutagenicity, hERG inhibitor, for drug-likeness analysis number of rotatable bonds, molecular weight, number of hydrogen bond donors, number of hydrogen bond acceptor and topological polar surface area respectively were studied. The lead compounds were further subjected to estimate their drug-like properties by using the Lipinski rule of five [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Molecular Dynamic Simulation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMolecular dynamics (MD) simulation study plays a decisive and vital role in the identification of potential small compounds for biological drug targets due to their ability to provide detailed interactions of ligand-protein interaction at the atomic level [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. MD simulations help the researchers study the conformational changes, binding events, and structural stability of both protein targets and ligands. Molecular dynamics studies bridge the gap between the structural information and the dynamic behavior of target proteins which helps in the rational design of potential drug candidates through a deeper understanding of their binding mechanisms and interactions with the various target proteins [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The simulations study and generation of trajectory files were performed by GROningen MAchine for Chemical Simulations (GROMAC) software [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The best docking conformations of selected ligands with both the target proteins of PDB ID 2NSD and 4FDO were selected for the MD simulation study. The CHARMM27 force field and simple point charge (SPC) water solvation models were selected for study. A cubic boundary box and the counter ion Na\u003csup\u003e+\u003c/sup\u003e Cl\u003csup\u003e-\u003c/sup\u003e of concentration 0.15M were added to neutralize the system. The energy minimization was performed by selecting the steepest descent algorithm as an EM integrator with 5000 steps. Simulation was conducted under the equilibration parameters NPT and NVT at 300k, 1 bar pressure and thermostat relaxation time of 100ps. Leap frog was selected as simulator and 100ns under simulation time were executed using mdrun program in GROMAC. Trajectories files were generate for analysing various dynamic parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), solvent accessible surface area (SASA), binding free energy estimate (MM-PBSA) and H-bonds [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Density functional theory (DFT) analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe density functional theory \u003cb\u003e(\u003c/b\u003eDFT) analysis was carried out to analyze the electronic properties of best identified hit obtained from virtual screening [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Gaussian 09 software tool has been used to perform geometry optimization and total energy calculations by using the Becke-3-Lee-Yang-Parr (B3LYP) function with the standard 6-311\u0026thinsp;+\u0026thinsp;+\u0026thinsp;G (d,p) basis set. Visualization of the structure and the analysis of the outputs were carried out with Gauss-View software [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Frontier Molecular Orbital (FMO) studies can predict the chemical reactivity of compounds and identify their stability. The calculated energies, the energy of the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and the gap between them were calculated to study the chemical stability of the identified molecule [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The chemical potential (\u0026micro;), chemical hardness (η), chemical softness (S), and global electrophilicity (ω) were calculated for the newly screened inhibitor. Mathematically, \u0026micro; index was derived according to the frontier molecular orbital LUMO and HOMO by using the Eq.\u0026nbsp;(2). The chemical hardness (η), chemical softness (S), and global electrophilicity (ω) were computed using the expressions (3), (4) and (5) respectively [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026micro; = (E\u003csub\u003eHOMO\u003c/sub\u003e +E\u003csub\u003eLUMO\u003c/sub\u003e) \u003cem\u003e/\u003c/em\u003e 2 (2)\u003c/p\u003e \u003cp\u003eη\u0026thinsp;=\u0026thinsp;E\u003csub\u003eLUMO\u003c/sub\u003e - E\u003csub\u003eHOMO\u003c/sub\u003e (3)\u003c/p\u003e \u003cp\u003eS\u0026thinsp;=\u0026thinsp;1\u003cem\u003e/\u003c/em\u003eη (4)\u003c/p\u003e \u003cp\u003eω\u0026thinsp;=\u0026thinsp;\u0026micro;2\u003cem\u003e/\u003c/em\u003e2η (5)\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result and Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Pharmacophore model design\u003c/h2\u003e \u003cp\u003eDuring pharmacophore model generation the data set of 58 compounds was divided into active and inactive sets of compounds. The PHASE module of the Schrodinger software was used to generate six features (A, D, H, R, N, P) based on 3D pharmacophoric models. The developed models help to predict the biological activity by prognosis of the features necessary for binding of ligands towards target protein. By using twenty-two active compounds from \u0026ldquo;pharmaset\u0026rdquo; we are generating the models having common pharmacophoric features to these active sets of compounds. The scoring and ranking of generated pharmacophoric models were done to identify the best hypothesis. The configuration of site points, the magnitude of vectors, selectivity, and activity with overall energies were considered for the scoring algorithm. Six point pharmacophore hypothesis which included features, one H-bond acceptor (A), two hydrophobic groups (HH) and three aromatic rings (RRR) denoted as AHHRRR is selected as the finest hypothesis based on its scoring (Table. 01).\u003c/p\u003e \u003cp\u003eThe top pharmacophore model with good predictive power for both active and inactive ligands was associated with the six-point hypothesis AHHRRR. Further, the predictability of a well pharmacophoric hypothesis was confirmed by considering its survival score and adjusted score. As the hypothesis, AHHRRR_1 has the highest survival score (5.222) and adjusted score (3.81) considered the best hypothesis for predicting the structural features required by both active and inactive ligands towards binding with its target protein to perform therapeutic action. The image of distances and angles between the pharmacophoric sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and hypothesis images for active ligand (compound no. 03) and inactive ligand (compound no. 18) are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec respectively. The pharmacophoric features (A) hydrogen bond acceptor mapped on etheric \u0026lsquo;O\u0026rsquo; atom present between imidazooxazine and methyl biphenyl ring, the two hydrophobic group (HH), first mapped on -CF3 group attached to 4th position of benzene ring and second is on oxazine ring of fused imidazooxazine ring. Among three aromatic rings (RRR) features, the first was present on the imidazole ring of the bicyclo imidazooxazine ring, and the second and third were present on biphenyl rings attached to imidazooxazine ring. The hypothesis reveals that the identified pharmacophoric features are essential for effective binding of ligands with the target proteins for showing anti-tubercular action..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Generation of atom based 3D-QSAR model\u003c/h2\u003e \u003cp\u003eThe atom-based 3D \u0026ndash;QSAR study was used to generate models relying on the alignment of the ligands in 3-dimensional space. The data set of 2-nitroimidazooxazines containing 58 compounds was divided into 41 training sets and 17 test set molecules randomly. The atom-based 3D-QSAR models were developed by using PHASE modules of Schrodinger software. The advantage of the PHASE algorithm is to get a 3D-contour map based on favorable and unfavorable regions. In the present study, the atom-based QSAR models were developed for training sets by considering partial least square (PLS) factor 8 and further validated by using test set compounds.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Analysis of developed QSAR models\u003c/h2\u003e \u003cp\u003eThe predictivity of the developed atom-based 3D-QSAR models with eight PLS factors was validated internally and externally for both training and test set compounds. The statistical parameters, squared correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e), cross-validated correlation coefficient (Q\u003csup\u003e2\u003c/sup\u003e), standard deviation of regression, variance ratio (F), Pearson\u0026rsquo;s correlation coefficient (Pearson-r), and root mean square error (RMSE), significance level of variance ratio (P) were used to evaluate the quality of the QSAR models. The summary of the statistical data of all the developed atom based QSAR models are listed in Table. 02. The PLS factor 8 model has the lowest standard deviation (SD) 0.1424 and the values of squared correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) for the training set and cross-validated correlation coefficient (Q\u003csup\u003e2\u003c/sup\u003e) for the test set compounds are 0.9525 and 0.8589 respectively indicates robustness in predictivity of the developed model for test set of compounds. Higher value of F (80.2), Pearson-r (0.8988) and other statistical parameters were also within the acceptance range implies that the built QSAR model is having good precision and used for further analysis and study. The linear scattered plots of actual versus predicted pIC\u003csup\u003e50\u003c/sup\u003e for training and test set are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb indicates the predictive ability of the generated QSAR model.\u003c/p\u003e \u003cp\u003eThe QSAR model was developed on the basis of features of the atoms attached to the core ring system such as hydrophobicity or non-polarity, positive ionic interaction, negative ionic interaction, electron-withdrawing effect, and other interactions. The result of atom type fraction contribution towards the developed atoms-based 3D- QSAR models is tabulated in Table. 03. The result of atom type fraction contribution reveals that the presence of hydrophobic or non-polar substitutions and electron withdrawing group plays a significant and important role towards the anti-tubercular activity whereas the presence of positive and negative ionic interaction groups has a mild role towards anti-TB activity.\u003c/p\u003e \u003cp\u003eDuring visualization of the developed atom-based 3D QSAR models in PHASE and study the correlation of activity with various atomic contributions was performed as colored cubes for both training and test set compounds. The developed QSAR models allowed us to find different atomic contributions like the presence of hydrophobic or non-polar groups, electron-withdrawing groups, and positive and negative ionic groups towards anti-tubercular activity. This method used atom types and their occupancy position in a grid of cubes for predicting properties and to visualize the regions that are favorable and unfavorable of the anti-tubercular activity. The maps generated for different atomic contributions in atom-based 3D QSAR are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-d for a training set compound (18) and Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-d for a test set compound (41). In these contour pictorial presentations of hydrophobic or non-polar interaction magenta colour cube shown is unfavourable and the green colour cube is favourable, for negative and positive ionic interaction yellow cube contribute positively while the red and purple cube contributes negatively. Lastly, for the electron-withdrawing map, the green color cube is favorable and the red color is unfavorable for the bioactivity of the ligand. The contribution map generated for the atom-based 3D-QSAR study indicates the required structural features for the interaction of ligands with its target protein. These maps further allow us to diagnose the particular atoms or groups attached to the core ring system that craves a particular physiochemical property to augment the anti-tubercular activity of ligands.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Molecular Docking Study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll the 58 compounds of the data set were docked into the binding pockets of the two most effective and potential drug target proteins for anti-tubercular action, NADH-dependent enoyl-ACP reductase (InhA) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) having PDB ID 2NSD and 4FDO respectively by PyRx tools version v0.8 using Autodock vina. The drug-binding scores in the form of kcal/mol for all the compounds are mentioned in TableS2. Afterward, compound 56 with the highest interaction energy of -8.2 kcal/mol and \u0026minus;\u0026thinsp;9.6 kcal/mol towards both the target proteins InhA and DprE1 was selected for analysis and used for retrieving the compounds having structural similarity up to 70% from the PubChem database. The selected compounds from the database were further screened by using docking, ADME analysis, and molecular simulations (MD) study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Structural similarity based Virtual Screening\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo find the effective multi-targeted anti-tubercular agent, compounds from the PubChem database were screened based on structure similarity by taking compound no 56 as a reference compound. About 237 ligands were identified and their structure was retrieved from the database for further screening. These compounds were then docked into the grid pockets of both target proteins having PDB ID 2NSD and 4FDO. Based on the significant docking scores of \u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;6.5 kcal/mol for both the targets only 09 compounds were selected for screening of their drug-like properties and ADMET predictions. For validation of the docking study, the co-crystallized ligand of the receptor was extracted and re-docked into the binding pockets of the respective target proteins. The result of the docking study of the top-ranked compounds has been reported in Table. 04. Post-docking analysis of the screened compounds reveals that compound CHEMBL566642 (MK3) showed the highest docking score of -9.2 and \u0026minus;\u0026thinsp;8.3 kcal/mol into the binding pockets of both the selected druggable targets for anti-tubercular activity. The 2D- 2D-dimensional docking interaction result of reference compound (56), screened compound (MK3), and co-crystallize ligands for both the receptors was reported in Table. 05. Upon examining the docking features of the identified hit (MK3) with target proteins InhA (2NSD) it was found that it has formed one H-bond with Ile194, П- П stacked interaction with Phe149, Ile194, and П-alkyl interaction with Ala157 and Ile215 residues at the active site of protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) and form two H-bond with Asn135, Asn144, carbon-hydrogen bond with Thr225, Glu190, Gly140, П- П stacked interaction with His415, П-alkyl interaction with Tyr226 and Ala139 residues present at active site of target protein DprE1(4FDO) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 In silico Drug likeness and Pharmacokinetic (ADME-T) analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter the structural similarity virtual screening, the best nine identified hits were utilized for drug-likeness and ADME-T predictions using the open web server ADME-T lab 2.0. The property of drug-likeness will be analyzed using the Lipinski rule of five violations. Further, other ADME-T properties like water solubility, pharmacokinetics, and toxicity of the ligands were analyzed. Drug-likeness studies qualitatively measure the chance of a molecule to turn into an oral drug concerning its bioavailability. The drug-likeness and Rule of Five prediction properties for the top nine compounds were summarized in Table. 06.\u003c/p\u003e \u003cp\u003eThe results exhibited that the nine screened compounds showed good drug-likeness with zero violation of rules. The acceptance value for all the parameters, molecular weight (\u0026le;\u0026thinsp;500), LogP (\u0026le;\u0026thinsp;5), number of hydrogen bond acceptors (0\u0026ndash;12), hydrogen bond donors (\u0026lt;\u0026thinsp;05), number of rotatable bonds (0\u0026ndash;11) and topological polar surface area (\u0026lt;\u0026thinsp;140\u0026Aring;2) showed significance oral bioavailability because of high membrane permeability. All the compounds having excellent synthetic accessibility score less than 06 to quantify the complexity of the molecular structure and ring system and can be synthesized easily. Advanced knowledge of pharmacokinetic and toxicity study results is helpful in the design of potential drug candidates with less toxicity. All the screened compounds were evaluated for their drug-like behavior through analysis of pharmacokinetic properties and toxicity study. The results are listed in Table. 07. For all the identified compounds, the LogS value is within the range of -4.5\u0026ndash;0.5 log mol/ltr indicating good aqueous solubility which is important for the estimation of absorption and distribution of drug within the body. The predicted value of plasma protein binding (PPB) and blood-brain barrier (BBB) comes within the acceptance range of 80\u0026ndash;90% and 0.0-0.3 for all the screened compounds respectively. The predicted value for human hepatotoxicity (H-HT) and drug-induced liver injury (DILI) are within the acceptance range revealing that the compounds caused hepatotoxicity in high doses. The acceptance value of the result for the Ames mutagenicity and skin sensitization study indicates that these compounds are safe from carcinogenicity and inflammatory skin reactions. All pharmacokinetic properties results fit well within the acceptance range defined for use in humans and reveal their potential as new multi-targeted anti-tubercular agents.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Prediction of anti-tuberculosis sensitivity\u003c/h2\u003e \u003cp\u003eFurther, the screened compounds were investigated to predict their minimum inhibitory concentration (MIC) against eight different Mycobacterium species by employing an online mycoCSM server [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Only Mycobacterium tuberculosis (MTB) MIC values were extracted and analyzed with marketed standards (Isoniazid and Rifampicin). The predicted MIC values calculated by mycoCSM are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e08\u003c/span\u003e. The result indicates that the MIC value of the hit molecule MK3 (-6.181\u0026micro;M) was close to the MIC value of rifampicin and higher than that of isoniazid. Lower MIC value indicates that less amount of this compound is required to inhibit the growth phase of the organisms, hence this compound may be selected as a potential anti-tubercular agent for further study.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Molecular Dynamic Simulation study\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMD simulation study is a computational technique that informs alternation in structure and behavior of protein occur throughout the simulation period. MD simulation also be helpful in the study of protein dynamics, folding, stability, and interaction of protein with ligands. In the present study, MD simulations were performed to verify the stability of the InhA-MK3 and DprE1-MK3 complex in physiological environments, which could not be achieved via molecular docking. Depending on scores of molecular docking the best screened compound (MK3) was selected for MD simulations analysis along with the reference compound (56) and co-crystallize ligands. Using Gromac software of 100 ns period, MD simulations were run by taking the best dock poses of the hit molecule with target proteins. The stability of the binding complex of MK3 with both the target proteins InhA and DprE1 was estimated by evaluating the plots of RMSD, RMSF, RoG, SASA, H-bonds, and binding free energy estimate (MM-PBSA).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 RMSD (Root Mean Square Deviation)\u003c/h2\u003e \u003cp\u003eRMSD measured the average distance between atom locations in the simulated structure and the initial reference structure which is an indicator of how far the molecular dynamics (MD) simulated structure has deviated from its initial configuration. A system with a lower RMSD value has less structural drift and is therefore more stable. The merge RMSD plot of reference compound (56), identified compound (MK3) and co-crystallize ligand with InhA protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) demonstrated stability in complex form. The complex of InhA- 56 became stable in the beginning and became unstable from 30-50ns and further stable from 70ns till the end. In the complex of InhA- MK3 steady confirmations were observed in the beginning followed by unsteady confirmations from 30- 65ns and again become linear till 100ns. Whereas in the complex of InhA- cocrystallize ligand showed variation in beginning and having steady RMSD value from 30-100ns. The RMSD plot for the DprE1 protein with the reference compound (56), identified compound (MK3), and co-crystallize ligand is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e8\u003c/span\u003eb indicates no significant deviation in comparison to the unbound protein. The plot for all three compounds shows stability across the whole simulation time of 100ns.\u003c/p\u003e \u003cp\u003eTherefore over the course of 100 ns simulation, the identified compound MK3 had a highly steady RMSD of 1.5, demonstrating that it maintained a constant binding mode. Hence it has a promising option for further development and improvement as a potent anti-tubercular agent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 RMSF (Root Mean Square Fluctuation)\u003c/h2\u003e \u003cp\u003eDuring MD simulation, Root-mean-square fluctuation (RMSF) was assessed to analyze the impact of lead compounds binding on the flexible portion of the targeted protein. RMSF result also estimates each residue's variations around its average location. Higher RMSF values indicate that the residues are more flexible. Finding flexible protein regions and stiff protein regions can be done with the help of RMSF. The identified hit molecule MK3 interacts with both InhA and DprE1 proteins showing stability in the residues during the simulation study. The RMSF analysis shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e9\u003c/span\u003ea and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e9\u003c/span\u003eb, the identified hit demonstrating more stability than the reference compound (56) and co-crystallize ligand complex with target proteins InhA and DprE1 respectively. However several residues such as Arg45, Phe109, Arg153 Ile202, Gly205, Trp249 and Leu269 for InhA complex and Thr8, Arg41, Phe267, Arg304, pro329, Phe362, Arg372 and Lys398 for DprE1 complex are highly flexible showed significant RMSF. This RMSF analysis indicates that the hit molecule has substantial stability in comparison to 56 and co-crystallize ligands for both target proteins. The presence of significant RMSF residues outside the active site of target proteins suggests that any conformational changes undergone may not have a substantial impact on the binding ability of MK3 into the active site of the target proteins of InhA and DprE1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 RoG (Radius of Gyration)\u003c/h2\u003e \u003cp\u003eThe Radius of Gyration (RoG) measures the dispersion of a protein\u0026rsquo;s mass around its center of mass, which helps to identify the expansion and compactness of protein structure. A folded structure or compact structure of the protein is identified if RoG is reduced and the unfolded structure of the protein is indicated in an increase in RoG value. Complexes of both target protein InhA and DprE1 with reference compound (56), identified compound (MK3), and co-crystallize ligand were analyzed for RoG and their results were given as Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e10\u003c/span\u003ea and \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e10\u003c/span\u003eb respectively. The RoG value was lower for MK3 for both complexes with InhA and DprE1 in comparison to 56 and co-crystallize ligands suggesting the structure of InhA- MK3 and DprE1- MK3 is more compact in comparison to other structures of InhA and DprE1 with 56 and co-crystallize ligand. This RoG study result indicates that MK3 structural stability during its interaction with both target proteins demonstrates its tendency to maintain a compact structure during simulation suggesting good RoG stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.6.4 SASA (Solvent Accessible Surface Area)\u003c/h2\u003e \u003cp\u003eThe surface area of protein that is accessible to the solvent is referred to as its \"solvent accessible surface area\" (SASA). Changes in SASA may be a sign of protein-ligand interactions, folding, ligand binding, or conformational changes. SASA is frequently used to examine the dynamics and stability of proteins. The complex of reference compound (56), identified compound (MK3), and co-crystallize ligand with InhA and DprE1 proteins were used for SASA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e11\u003c/span\u003ea and b). The study showed a very slight deviation during the simulation due to minor structural changes during complex formation. The complex of MK3 with InhA and DprE1 confirms that this compound revealed acceptable stability.\u003c/p\u003e \u003cp\u003eThe results from the RMSD, RMSF, and RoG studies were further supported by the SASA measurements, which provided additional information on the stability of MK3 in interaction with InhA and DprE1 target proteins for anti-tubercular action.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.6.5 H-bonds Analysis (Hydrogen Bonds Analysis)\u003c/h2\u003e \u003cp\u003eH-bond analysis during MD simulation illustrates the stability of the protein-ligand complex. The H-bond analysis for the complex of reference compound (56), identified hit (MK3), and co-crystallize ligand with target protein InhA and DprE1 were given in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e12\u003c/span\u003ea and \u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e12\u003c/span\u003eb. The H-bonding interaction plot describes that the screened compound MK3 forms an average of 2\u0026ndash;4 and 2\u0026ndash;5, H-bond within the target site of InhA and DprE1 protein respectively during the simulation study. From the graph, it is evident that the H-bonds formed between the ligand and amino acids of the target protein were conserved during the 100ns simulation for both complexes. In conclusion, the H-bond analysis supported the results of previous structural investigations by providing further evidence of the stability of MK3 in contact with both InhA and DprE1 proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.6.6 Binding free energy calculation (MM-PBSA)\u003c/h2\u003e \u003cp\u003eBinding free energy calculation enables the energetic stability of the protein-ligand complex to be assessed, and the binding strength between them to be predicted. The gmx_mmpbsa tool of GROMACS is used for the calculation of the binding free energy of the ligand-protein complex. This gmx_mmpbsa applies the Molecular Mechanic- Poisson-Boltzmann Surface Area (MM-PBSA) method for binding energy calculation. The binding free energy was calculated for the docked complexes of target proteins InhA and DprE1 with compounds 56, MK3 and co-crystallize ligands by using expression 6;\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eΔG\u003csub\u003ebind\u003c/sub\u003e = G\u003csub\u003ecomplex\u003c/sub\u003e - (G\u003csub\u003eprotein\u003c/sub\u003e + G\u003csub\u003eligand\u003c/sub\u003e) (6)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere G\u003csub\u003ecomplex\u003c/sub\u003e is the energy of the protein-ligand complex, G\u003csub\u003eprotein\u003c/sub\u003e and G\u003csub\u003eligand\u003c/sub\u003e are the energy of protein and ligand in aqueous solvent, respectively. Other energies like van der Waals energy (E\u003csub\u003evdw\u003c/sub\u003e), electrostatic energy (E\u003csub\u003eelec\u003c/sub\u003e), polar solvation energy (G\u003csub\u003epolar\u003c/sub\u003e), non-polar solvation energy (G\u003csub\u003enonpolar\u003c/sub\u003e) were also calculated and shown in Table.09.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBinding free energy calculation indicates that E\u003csub\u003evdw\u003c/sub\u003e, E\u003csub\u003eelec,\u003c/sub\u003e and G\u003csub\u003enon-polar\u003c/sub\u003e have a major contribution to the total binding energy because of their negative values but G\u003csub\u003epolar\u003c/sub\u003e has no contribution because of its positive value. Compound MK3 is found to have a very good binding energy of -42.27 kJ/mol and \u0026minus;\u0026thinsp;32.39 kJ/mol with the target proteins InhA and DprE1 respectively which suggest that this compound has strong and effective interaction within the active binding pockets of these proteins.\u003c/p\u003e\u003cp\u003eAll taken together, these results of the simulation study suggest that the screened ligand MK3 has a strong affinity and energy stability, which suggests that this could be used as a potential InhA and DprE1 inhibitor. These findings are promising and highlight that the compound MK3 can be used as a potential multi-targeted anti-tubercular agent.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.7 DFT Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe DFT analysis was carried out for the compound MK3 to analyze its electronic properties and chemical reactivity by Gaussian 09 software tool by using the Becke-3-Lee-Yang-Parr (B3LYP) function with the standard 6-311\u0026thinsp;+\u0026thinsp;+\u0026thinsp;G (d,p) basis set. Visualization of the structure and the analysis of the outputs were carried out with Gauss-View software. FMO analysis of the identified compound was performed by measuring the energy of the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and the gap between them. Measurement of HOMO and LUMO designate the nucleophilicity and electrophilicity nature of the compound. The reactivity of the compound is checked by the energy gap between HOMO and LUMO, the compound having a small energy gap allows the molecule to be more reactive but less stable whereas the compound having a higher difference margin is less reactive but more stable. Along with HOMO and LUMO energy global reactivity indices such as chemical potential (\u0026micro;), chemical hardness (η), chemical softness (S), and global electrophilicity (ω) were calculated for the newly screened inhibitor MK3 to check its bioactivity. The results of the study are represented in Table. 10. Furthermore, the Frontier Molecular Orbitals (FMOs) of the screened molecule are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003e. The derived values of ΔEgap and global indices indicate significant chemical and bio-reactivity of the identified molecule MK3.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTuberculosis is regarded as one of the fatal infections caused by Mycobacterium tuberculosis in humans, which triggers mobility and morbidity throughout the world because of the upbringing of drug resistance cases. With the aim of reducing the duration of treatment against resistant MTB species and cost of developing new drug-candidates, this study integrated various computer-aided drug design (CADD) techniques to design new potent molecules against two potential anti-tubercular drug targets InhA and DprE1 through virtual screening by integrating similar structure-based drug discovery techniques. We performed atom-based 3D-QSAR analysis, ADMET profiling, molecular docking, MD simulation, and DFT analysis to characterize the title molecule and assess its potential use against drug resistant species of MTB. Through a rigorous assessment process, predictive validated atom-based 3D-QSAR and pharmacophore hypothesis models were used to identify molecular descriptors that exert an influence on the improvement of anti-tubercular activity. The proposed compounds MK1-MK9 confirmed the Lipinski rule of five. During the process of molecular docking, we observed that compound MK3 showed the highest binding energy of -9.2 and \u0026minus;\u0026thinsp;8.3 kcal/mol, indicating it as a lead-like drug. Furthermore, it exhibited the most favorable interaction with the residues in the binding pockets of the target receptors, InhA and DprE1. ADME-T results showed good absorption, no penetration into the brain, and non-toxic for the newly identified molecule. Further, the 100 ns MD simulation results endorses the findings of the molecular docking result and indicates that the compound, MK3 showed stable interactions with effective RMSD, RMSF, RoG, SASA, and H-bond formation within the active pockets of InhA and DprE1 proteins. In MM-PBSA analysis, the compound MK3 has a binding energy of -42.27 kJ/mol and \u0026minus;\u0026thinsp;32.39 kJ/mol towards InhA and DprE1 proteins, suggesting this molecule has strongest and effective binding within the active pockets of these proteins. Again, the result of DFT analysis suggests that molecule MK3 tends to exhibit more active anti-tubercular action because of having a smaller ΔEgap of 0.14806 between HOMO and LUMO. After validating all the theoretical results, the identified molecule MK3 has a great chance to work as a new inhibitor of the two most druggable targets InhA and DprE1 for treating tuberculosis, and the outcome of this computation study proposed that the compound MK3 can develop as a potent multi targeted anti-tubercular agent in the future.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors express their gratitude to Schrodinger office, Bangalore, India; and SiBIOLEAD, Little Rock, Arkansas, USA, for the help rendered in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) reported no funding associated with the work featured in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.P- performed the work and wrote this manuscript. 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J Chem Inf Model 60:3450\u0026ndash;3456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1021/acs.jcim.0c00362\u003c/span\u003e\u003cspan address=\"10.1021/acs.jcim.0c00362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 01\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eScore of multiple Pharmacophore hypothesis AHHRRR\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypoID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvival score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSelectivity score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInactive score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVolume score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of matches\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBEDROC score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHHRRR_7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 02\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of atom based 3D- QSAR results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e# Factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e CV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Scramble\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson-r\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.82E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.28E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.82E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.48E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.65E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eFactors: Number of factors in the partial least squares regression model; SD: Standard deviation of the regression; R\u003csup\u003e2\u003c/sup\u003e: Value of R\u003csup\u003e2\u003c/sup\u003e for the regression; R\u003csup\u003e2\u003c/sup\u003e CV: Cross-validated R\u003csup\u003e2\u003c/sup\u003e value, computed from predictions obtained by a leave-N-out approach; R\u003csup\u003e2\u003c/sup\u003e Scramble: Average value of R\u003csup\u003e2\u003c/sup\u003e from a series of models built using scrambled activities; Stability: Stability of the model predictions to changes in the training set composition. This statistic has a maximum value of 1; F: Variance ratio. Large values of F indicate a more statistically significant regression; P: Significance level of variance ratio. Smaller values indicate a greater degree of confidence; RMSE: Root-mean-square error of the test set; Q\u003csup\u003e2\u003c/sup\u003e: Value of Q\u003csup\u003e2\u003c/sup\u003e for the predicted activities of the test set; Pearson-r: Value of Pearson-R for the predicted activities of the test set\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 03\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAtom type fraction contribution of atom based 3D- QSAR models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e# Factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHydrophobic/non-polar\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative ionic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive ionic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectron-withdrawing\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 is available in the Supplementary Files section.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 05\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocking interactions result of the reference, screened and co-crystallize ligands with amino acid residues of target proteins.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePDB ID-2NSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePDB ID-4FDO\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eП- \u0026sigma;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eП- П stacked\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eП-alkyl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-H bond\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eП-alkyl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eП- П stacked\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlkyl\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg173,Glu169,\u003c/p\u003e\n \u003cp\u003eSer166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVal163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhe108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer228,\u003c/p\u003e\n \u003cp\u003eLys134,Tyr415,Gly321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg58,Val365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeu365,Arg58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhe149,\u003c/p\u003e\n \u003cp\u003eIle194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro193,\u003c/p\u003e\n \u003cp\u003eMet199,\u003c/p\u003e\n \u003cp\u003eMet161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsn135,Asn144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThr225,Glu190,Gly140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyr226, Ala139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHis145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo-crystallize ligand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla157,\u003c/p\u003e\n \u003cp\u003eIle215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrp16, Lys418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGly321,Thr118,\u003c/p\u003e\n \u003cp\u003ePhe320,Trp230, Gly117,\u003c/p\u003e\n \u003cp\u003eIle131,\u003c/p\u003e\n \u003cp\u003ePro116,Ala117,\u003c/p\u003e\n \u003cp\u003eSer59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVal365,Leu363, Leu317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyr60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVal121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u003cstrong\u003eTable. 06 Predictions of Drug-likeness and Rule of Five for the top screened compounds\u003c/strong\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enHA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enHD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTPSA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enRot\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSynth\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMCE-18\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLipinski\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e411.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e343.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e410.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e349.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e410.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e349.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e342.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e336.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccepted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eMW\u0026thinsp;=\u0026thinsp;Molecular weight (\u0026le;\u0026thinsp;500), Vol\u0026thinsp;=\u0026thinsp;vander Waal\u0026rsquo;s volume, LogP\u0026thinsp;=\u0026thinsp;Distribution coefficient (\u0026le;\u0026thinsp;5) ,nHA\u0026thinsp;=\u0026thinsp;Hydrogen bond acceptor(0\u0026ndash;12),nHD\u0026thinsp;=\u0026thinsp;Hydrogen bond donor (\u0026le;\u0026thinsp;5), TPSA\u0026thinsp;=\u0026thinsp;Topological Polar Surface area(˂140),nROT\u0026thinsp;=\u0026thinsp;Number of rotatable bond (0\u0026ndash;11), Synth\u0026thinsp;=\u0026thinsp;Synthetic accessibility Score (1\u0026ndash;6 (excellent), \u0026gt; 6 (poor)), MEC-18\u0026thinsp;=\u0026thinsp;Medicinal chemistry Evaluation 2018 (\u0026ge;\u0026thinsp;45 excellent)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 07\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePharmacokinetic (ADME) and Toxicity prediction results for the top screened compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCaco-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHIA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBBB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-HT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDILI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkinSen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLC50\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eLogS\u0026thinsp;=\u0026thinsp;Logarithm of aqueous solubility (-4.5 to 0.5 log mol/L), Caco-2\u0026thinsp;=\u0026thinsp;human colon adenocarcinoma cell lines permeability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026gt;-5.15\\text{l}\\text{o}\\text{g} \\text{c}\\text{m}/\\text{s}.\\)\u003c/span\u003e\u003c/span\u003e), HIA\u0026thinsp;=\u0026thinsp;Human intestinal absorption (0-0.3: excellent), PPB\u0026thinsp;=\u0026thinsp;Plasma protein binding (\u0026thinsp;\u0026gt;\u0026thinsp;80%), BBB\u0026thinsp;=\u0026thinsp;Blood brain barrier penetration (0-0.3: excellent ; 0.3\u0026ndash;0.7: medium ; 0.7-1.0: poor ), H-HT\u0026thinsp;=\u0026thinsp;The human hepatotoxicity (0\u0026ndash;1), DILI\u0026thinsp;=\u0026thinsp;Drug-induced liver injury (0\u0026ndash;1), Ames\u0026thinsp;=\u0026thinsp;The Ames test for mutagenicity(0\u0026ndash;1),Skinsen\u0026thinsp;=\u0026thinsp;Skin sensitization (0\u0026ndash;1), LC50\u0026thinsp;=\u0026thinsp;Lethal concentration cause death after 96 hours\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 08\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnti-TB activity prediction of screened hits through online server mycoCSM\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted MTB. MIC (log \u0026micro;M)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsoniazid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRifampicin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 09\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMM-PBSA calculations for the complex of DprE1 and InhA protein with compound 56, MK3 and co-crystallize ligands\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCompound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eComplex of InhA with ligands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eComplex of DprE1 with ligands\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003evdw\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003eelec\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG\u003csub\u003epolar\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG\u003csub\u003enon\u0026minus;polar\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e△G\u003csub\u003ebind\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003evdw\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003eelec\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG\u003csub\u003epolar\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eG\u003csub\u003enon\u0026minus;polar\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e△G\u003csub\u003ebind\u003c/sub\u003e (kJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-36.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-28.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-40.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-52.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-42.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-50.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-32.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo-crystallize ligand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-42.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-34.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-37.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-25.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.10 Global indices of the screened compound CHEMBL566642\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.086687306501547%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"85.91331269349845%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Indices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.91891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHOMO (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.37837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLUMO (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792792792792794%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta; E\u003csub\u003egap\u003c/sub\u003e (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.693693693693694%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.792792792792794%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta; (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.072072072072071%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.35135135135135%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026omega; (ev)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.086687306501547%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.25386996904025%\" valign=\"top\"\u003e\n \u003cp\u003e-0.26241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.990712074303406%\" valign=\"top\"\u003e\n \u003cp\u003e0.14806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.990712074303406%\" valign=\"top\"\u003e\n \u003cp\u003e0.14806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.371517027863778%\" valign=\"top\"\u003e\n \u003cp\u003e6.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75232198142415%\" valign=\"top\"\u003e\n \u003cp\u003e0.11981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":false,"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":"Atom based 3DQSAR, Molecular docking, InhA inhibitor, DprE1 inhibitor, ADME-T, Molecular Dynamic simulation, DFT","lastPublishedDoi":"10.21203/rs.3.rs-4002518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4002518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTuberculosis (TB) has become the biggest threat towards human society due to the rapid rise in resistance of the causative bacteria Mycobacterium tuberculosis (MTB) against the available anti-tubercular drugs. There is an urgent need to design new multi-targeted anti-tubercular agents to overcome the resistance species of MTB through computational design tools. With this aim in the present work, a combination of atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), six-point pharmacophore (AHHRRR), and molecular docking analysis was performed on a series of fifty-eight anti-tubercular agents. The generated QSAR model showed statistically significant correlation co-efficient R\u003csup\u003e2\u003c/sup\u003e, Q\u003csup\u003e2\u003c/sup\u003e, and Pearson r-factor of 0.9521, 0.8589, and 0.8988 respectively indicating good predictive ability. Molecular docking study was performed for the data set of compounds with the two important anti-tubercular target proteins, Enoyl acyl carrier protein reductase (InhA) (PDBID: 2NSD) and Decaprenyl phosphoryl-β-D-Ribose 20-epimerase (DprE1) (PDBID: 4FDO). Using the similarity search principle virtual screening was performed on 237 compounds retrieved from the Pubchem database to identify potent multitargeted anti-tubercular agents. The screened compound, MK3 showed the highest docking score of -9.2 and \u0026minus;\u0026thinsp;8.3 Kj/mol towards both the target proteins InhA and DprE1 were picked for 100ns molecular dynamic simulation study using GROMACS. From the data generated, the compound MK3 showed thermodynamic stability and effective binding within the active binding pocket of both target proteins without much deviation. The result of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and energy gap analysis predicts the molecular reactivity and stability of the identified molecule. Based on the result of the above studies the proposed compound MK3 can be successfully used for the development of a novel multi-targeted anti-tubercular agent with high binding affinity and favourable ADME-T properties.\u003c/p\u003e","manuscriptTitle":"Computational approaches: Atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted Anti-tubercular agents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 08:19:05","doi":"10.21203/rs.3.rs-4002518/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":"3d269b63-7cc0-46e5-9db4-2a7f4045107a","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-05T18:15:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 08:19:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4002518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4002518","identity":"rs-4002518","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}