In silico study of PDE10A inhibitors for schizophrenia disease: Molecular docking, ADMET and DFT analysis

In silico study of PDE10A inhibitors for schizophrenia disease: Molecular docking, ADMET and DFT analysis | 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 In silico study of PDE10A inhibitors for schizophrenia disease: Molecular docking, ADMET and DFT analysis Ashutosh Kharwar, Anjani Kumar Tiwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6532706/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Recently, interest in phosphodiesterase 10A inhibitors has increased for schizophrenia treatment. Medicinal chemists have extensively worked on developing potent PDE10A inhibitors with minimal side effects. However, despite these efforts, PDE10A inhibitors have yet to gain approval for treating neurodegenerative disorders, possibly due to limited research in this area. In this study, we used an in-silico approach to evaluate 100 novel compounds derived from pyrazine, quinazoline, triazine, hydrazone, and cinnoline for their interaction with the PDE10A receptor (PDB ID: 3HQY) through molecular docking. Based on their drug-like properties, including physicochemical characteristics and ADMET profiles, eight top-ranking compounds, comparable to the standard drug PF6, were selected. We further narrowed this down to six highly promising molecules and identified protein targets for the PDE10A compound using a target prediction tool. Further investigations, including FMO (Frontier Molecular Orbital) and MEP (Molecular Electrostatic Potential) studies, showed increased stability in the drug complexes due to a larger HOMO-LUMO gap. Additionally, a significant electrophilicity index indicated favorable electrophilic behavior and increased reactivity of the drugs. Overall, a detailed examination has identified new favorable sites for bond formation in the 6 anticipated analogs, suggesting their potential drugs for treating schizophrenia diseases. PDE 10A Molecular docking ADMET DFT MEP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction A significant unmet medical need is schizophrenia, a mental illness. Schizophrenia affects an estimated 0.3–0.7% of the population, and its social consequences are significant[ 1 ]. A single adverse effects including weight gain, hyperglycemia, dyslipidemia, and extrapyramidal symptoms have been related to the pharmacology of atypical antipsychotic drugs, which block the dopamine and serotonin receptors.[ 2 , 3 ]. These medications are the mainstay of contemporary schizophrenia treatment. The cognitive impairments associated with schizophrenia have also been linked to dysregulation of the glutamate system[ 4 ]. The use of phosphodiesterase 10A (PDE10A) inhibitors to treat schizophrenia has gained popularity recently. PDE10A is a dual substrate phosphodiesterase that is widely expressed in the striatal region of the brain. It hydrolyses two second messengers, cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP)[ 5 ]. Because PDE10A operates downstream of dopamine and glutamate receptors within their signalling pathways, and given that striatal dysfunction is linked to the pathophysiology of schizophrenia, directly inhibiting PDE10A could elevate cGMP and cAMP levels in the striatum. This may improve cognitive deficits and behavioural control in schizophrenia without the adverse side effects associated with current standard treatments[ 6 ]. Eleven PDE families make up the PDE superfamily, which generates more than fifty functionally distinct enzymes[ 7 ]. Nonetheless, there is a 25–35% amino acid similarity among PDE families, which makes the creation of specific PDE10A inhibitors a possible objective[ 8 , 9 ]. Furthermore, it has been proposed that blocking PDE10A offers a new way to treat Alzheimer's by counteracting the negative effects of amyloid-β peptide on the cAMP-response element-binding protein (CREB) pathway, a crucial regulator of the formation of long-term memory[ 10 ]. The entire process of drug development has been impacted through computational drug design and discovery approaches developed over the past 20 years [ 11 ]. In order to comprehend the molecular basis of PDEs inhibition, computational chemistry was first applied in the 1980s[ 12 ]. The first study to be published made an effort to describe the pharmacophore for PDE inhibitors by attempting to explain the association between particular physicochemical qualities and efficacy of existing inhibitors[ 13 ]. The goal of medicinal chemists has been to create strong PDE10A inhibitors using structure-based and ligand-based methods, aiming to minimize side effects[ 14 , 15 ]. Despite these positive developments, scientists are still having difficulty developing drugs that work. In silico modelling techniques, a fruitful and economical technology in the design of novel lead compounds should be employed in conjunction with experimental procedures to streamline the drug discovery process[ 16 , 17 ]. In the pursuit of designing a novel molecule targeting Schizophrenia, specifically focusing on PDE10A derivatives, we have assembled a collection of 100 distinct compounds. These compounds feature monocyclic heterocyclic rings, drawing from derivatives of pyrazine, quinazoline, triazine, hydrazone, cinnoline, and other promising candidates identified in preclinical models of schizophrenia[ 18 – 26 ]. Computational studies were then conducted across five stages to streamline the selection process and identify the most promising molecules. Initially, molecular docking was performed to assess the binding affinity of the compounds, with screening based on Hyde scores. Subsequent stages involved evaluations of physicochemical properties, pharmacokinetics, toxicity, target prediction, and drug-likeness with a focus on identifying the most potent candidates within the parameters set by relevant software tools. Lastly, computations based on quantum mechanics were used to examine the chosen drug compounds' stability, reactivity and electronic characteristics. Parameters consisting of HOMO-LUMO gaps in energy, softness, chemical potential, hardness, electrophilicity index and electronegativity were examined in this context. This comprehensive study aims to investigate the inhibitory activities of the identified compounds using in silico research, with the ultimate goal of developing PDE10A inhibitors as potential candidates for Schizophrenia treatment. The screening process yielded a PDE10A fragment showing potent and selective inhibitory activity, demonstrating effectiveness in a preclinical test for schizophrenia. 2. Methodology 2.1 Docking validation, protein preparation, and ligand preparation A series of Hundred (100) molecules of pyrazine, quinazoline, triazine, hydrazone and cinnoline derivatives were extracted from the published article. Using the Chemdraw program, the 2D structures of 100 molecules were created. After that, optimization was performed using the force field (MMFF) with Chem3D software in accordance with our earlier research[ 27 – 33 ]. The PDE10A protein's X-ray crystal structure (PDB ID: 3HQY) was obtained from the protein data bank website ( https://www.rcsb.org ). It made it possible to identify certain proteins' locations within the human body. LeadIT Version 2.3.2 (BioSolveIT, Germany, 2018) was used to integrate the crystal structure of the PDE10A protein after sufficient XRD data, R-value, amino acid residues, chain number and resolution were achieved. Next, "Prepare receptor" was used to prepare the structure. In summary, the original PDB structure was processed by selecting both chains A, and four binding pockets of amino acids were used to carry out an additional automatic detection method. Prior to docking the molecular dataset, the redocking procedure validated the use of LeadIT. Using the redocking procedure, the ligand bound from the X-ray crystal structure was docked back into the binding pocket of the enzyme.[ 34 ]. This was done to ensure that LeadIT could replicate the orientation and placement of the inhibitors as they were shown in the X-ray structure. [ 35 ]. 2.2 Molecular Docking LeadIT, part of the BioSolveIT Package, facilitated docking studies of custom-designed ligands within target receptors, maintaining a 6.5 spacing among amino acid residues. Following docking, potential ligand-protein interactions were explored using HYDE assessment.[ 36 ]. Docking calculations adhered to software instructions, generating ten poses, each with a binding affinity for the PDE10A site measured in kJ/mol. Docking scores, L.E. values and estimated affinities from HYDE score function were assessed to select optimal poses[ 37 , 38 ]. According to the logP atomic increment system, the function of empirical scoring of HYDE (Eq. 1) uses terms for hydrogen bonding, hydration and atom-specific desolvation (i.e., without weighting factors). The amount of non-hydrogen atoms in the molecule and the quotient of ∆G (Eq. 2) were used to calculate the ligand binding affinity. ∆G Hyde =∑ atom i [∆G i Dehydration +∆G i H−bonds ] …… (1) LE=∆G ∕ N …… (2) Where ∆G = -RTlnK i N = number of non-hydrogen atoms The visualization was carried out via the Discovery Studio Visualizer program[ 25 ]. 2.3 Predicting toxicity, drug likeness and pharmacokinetics Potential drug candidates were found through the use of the Swissadme online tool ( www.swissadme.ch ), which is used to predict various pharmacokinetic, physicochemical parameters, and drug-likeness characteristics of compounds in silico. pkCSM ( https://biosig.lab.uq.edu.au/pkcsm ) and ADMET lab 2.0 ( https://admetmesh.scbdd.com/ ) were used to measure a variety of toxicity parameters[ 39 – 41 ]. 2.4 Bioactivity score The Molinspiration online software ( www.molinspiration.com ) was utilized to forecast the bioactivity of the acids that were being studied. G protein-coupled receptors (GPCR ligands), ion channel modulators, nuclear receptor ligands, kinase inhibitors, protease inhibitors, and enzyme inhibitors were among the targets for which activity scores were computed. The online SwissTarget-prediction webtool ( http://www.swisstargetprediction.ch/ ) was also utilized to obtain an accurate protein target prediction.[ 42 , 43 ] 2.5 Evaluation of Chemical Reactivity using DFT By analyzing the electrical characteristics of the six compounds, the density functional theory has been used to show that our target molecules are chemically stable. Using the 6-31G + (d, p) basis set and the B3LYP hybrid functional, the Gaussian 09 software program carried out the DFT calculations. GaussView was used to display the results. Electronic parameters in the gaseous state, such as the molecular electrostatic potential, quantum chemical reactivity descriptors, HOMO-LUMO band gap, LUMO power value (ELUMO), and energy value of the HOMO (EHOMO), were calculated based on the optimized structure. The following formulas were used to determine the quantum chemical reactivity descriptors, such as softness (S), electronegativity (χ), chemical potential (µ), hardness (Ƞ) and electrophilicity index (ω)[ 44 , 45 ]. 3. Results and discussion 3.1 Protocol validation for docking Resolution, sequence length, R-value, and Hyde score values are among the characteristics of PDE10A receptors shown in Table 1 . The protein superimposition between the observed X-ray crystallographic structure posture and the docked molecule pose, as seen in Fig. 1 . The root mean square deviation (RMSD) contrasting these two positions is measured at 1.1 Å. The compound's estimated RMSD values align closely with this measurement, with a value of 1.1 Å, which falls below the threshold of 2 Å. This confirms the accuracy and reliability of the method employed in this study. Table 1 Protein (3HQY) properties, Hyde score and RMSD detail Enzymes ID Amino acids number Resolution (Å) R-Value Co-crystallized ligand Hyde score (KJ/Mol) RMSD (Å) 3HQY 380 2.00 0.195 PF6 -58 1.1 3.2 Docking analysis of screened compounds Molecular docking serves as a valuable modeling technique utilized to evaluate the binding efficacy of drugs to target receptors. Central to this process is the assessment of affinity, or "scoring," which involves gauging the ligand's interaction within the binding pocket. In our approach, ligand-binding affinity is predicted using the HYDE technique, which also computes the realistic free energies involved in ligand-protein binding. A higher negative value denotes a higher ligand-protein complex binding affinity. We selected 100 previously reported compounds for binding assessment against PDE10A inhibitors using PDB ID: 3HQY for docking, with PF6 serving as the co-crystallized ligand. Table 2 lists the various docking analysis sub-parameters along with the renamed selected compounds. Notably, compounds L1 to L8 exhibited superior binding affinity compared to the co-crystallized ligand PF6. In terms of its binding affinity per heavy atom, ligand potency is quantified by ligand efficiency (L.E.) Higher (or close to reference) ligand efficiency values for all compounds indicate more efficient ligands, except for L1 and L2. The lipophilic contact area of a protein represents the degree of lipophilic interactions between its hydrophobic regions and ligand. All ligands exhibited higher lipophilic contact area values compared to the reference PF6. The interaction within lipophilic and hydrophilic areas is quantified by the ambiguous score. Higher values of the ambiguous score for all compounds indicate a enhanced capacity to balance interactions in both kinds of scenarios. Compounds L2, L3, L5, L6, and L7 exhibited high Clash penalty scores, suggesting unfavorable steric interactions that could potentially impact binding. Table 2 PDE 10A derivatives docking results involving PDB ID: 3HQY Compound Renaming of compound H.S. (KJ/mol) L.E. LIPO AMBIG CLASH 36 L-1 -62 0.39 -21.1720 -8.8751 4.6082 35 L-2 -60 0.39 -19.1865 -9.1879 8.6474 66 L-3 -60 0.51 -19.4875 -8.8961 6.3741 96 L-4 -60 0.44 -17.5705 -8.6344 3.762 65 L-5 -59 0.51 -18.937 -8.4303 5.9119 67 L-6 -59 0.51 -19.5586 -9.268 6.5745 91 L-7 -59 0.45 -18.2129 -10.7909 14.0178 95 L-8 -59 0.46 -17.8514 -9.5954 4.6477 PF6 PF6 -58 0.46 -18.2079 -8.2058 5.4661 3.3 Binding interaction and interpretation of selected compounds The two-dimensional (2D) interactions between protein-ligand complexes were analyzed using Discovery Studio software, with findings summarized in Table 3 and depicted in Fig. 2 . These visuals provide insights into binding affinities and interactions that are non-bonding. Hydrogen bonds, pi-pi bonds, and van der Waals forces were identified as the significant interactions between PDE10A and the selected ligands. These interactions play crucial roles in figuring out the drug-receptor interface's binding affinity and eventually affect the efficacy of the medicine by influencing the stability of the ligands at the target sites. Specifically, compounds L2 to L8, and the reference ligand demonstrated hydrogen bonding with amino acid residues such as serine, glutamine, glycine, and tyrosine. Notably, compound L1 did not exhibit interactions with tyrosine residues. Additionally, interactions involving water molecules and hydrophobic regions, indicative of poor water solubility, were observed. These interactions are characterized by hydrophobic interactions within the protein structure. These findings provide valuable insights into the molecular interactions between the selected compounds and PDE10A, contributing to our understanding of their potential as effective drug candidates. Pi-pi bonds, a common hydrophobic interaction, have been found in L1, L2, L3, L4, L5, L6, L8 including reference PF6 is PHE-719 and GLU-711 except compound L7. Another common hydrophobic interaction with PHE phenylamine and PRO proline which are found in all compounds and standard PF6 (except PHE-719 is absent in L7 and PRO-702 absent in L1). Each compound exhibits an increased quantity of residues engaged in Van der Waals interactions compared to the standard PF6, with the exception of L1, which only interacts with two residues: ASP-664 and GLY-715. Table 3 Results of interactions between PDE 10A derivatives and PDB ID: 3HQY Compound H-BOND HBI (Pi-Pi bond) Van der Waals L1 SER-667 GLN-716 PHE-686 PHE-719 ASP-664 GLY-715 L2 TYR-683 GLN-716 PHE-686 PRO-702 GLU-711 PHE-719 LEU-625 LEU-665 SER-667 VAL-668 TRP-687 LYS-708 ARG-709 GLU-711 VAL-712 GLY-715 VAL-723 L3 TYR-683 GLN-716 PRO-702 GLU-711 PHE-719 LEU-625 LEU-665 SER-667 THR [k] -675 PHE-686 TRP [l] -687 LYS [m] -708 ARG [n] -709 GLY-715 TYR-720 L4 HIS-515 TYR-683 GLY-715 PHE-686 PRO-702 GLU-711 PHE-719 MG-2 HIS [o] -557 SER-561 LEU-625 LEU-665 SER-667 VAL [p] -668 LYS-708 ARG-709 VAL-712 GLN-716 L5 TYR-683 GLN-716 GLU-711 PHE-719 LEU-625 LEU-665 SER-667 THR-675 PHE-686 TRP-687 LYS-708 ARG-709 VAL-712 GLY-715 TYR-720 L6 TYR-683 GLN-716 PRO-702 GLU-711 PHE-719 LEU-625 LEU-665 SER-667 THR-675 PHE-686 LYS-708 ARG-709 VAL-712 GLY-715 TYR-720 TRP-752 L7 TYR-683 GLY-715 PRO-702 HIS-515 HIS-519 CYS [q] -666 VAL-668 THR-675 PHE-686 TRP-687 LYS-708 ARG-709 GLU-711 TYR-720 TRP-752 L8 HIS-515 TYR-683 PRO-702 GLU-711 PHE-719 PHE-625 LEU-665 SER-667 PHE-686 LYS-708 ARG-709 GLY-715 GLN-716 TYR-720 PF6 TYR-683 SER-667 PHE-686 PRO-702 GLU-711 PHE-719 TYR-514 HIS-515 LEU-625 ASP-664 ALA-679 LYS-708 ARG-709 VAL-712 3.4 Physicochemical, drug-likeness and pharmacokinetics assessment of screened compounds The Swissadme online web tool was used to evaluate the physicochemical, drug-likeness, and pharmacokinetic features of the top 8 compounds that were chosen. 48 descriptions that cover both physiologically and pharmaceutically significant parameters are included in this web tool. We identified 14 criteria, detailed in Table 4 , that indicate appropriate ranges for drug-like molecules. Observations on the physicochemical properties revealed that the molecular weight (MW) of compounds L2 to L8 falls within the range of 150–450 g/mol, with all compounds exhibiting a flexibility (rotatable bonds) of no more than nine. TPSA of our compounds ranged among 20–130 Å 2 , indicating oral bioavailability. The consensus log Po/w value, a measure of lipophilicity, ranged from − 0.7 to + 5.0 for all compounds, except L1 and L2, suggesting potential for efficient translocation across biomembranes. L2 and PF6 were insoluble, although the SILICOS-IT model predicted that compounds L1, L3, L4, L5, L6, L7, and L8 would have poor solubility. Evaluation of gastrointestinal (GI) absorption indicated high values for all compounds, suggesting drug-like properties. Additionally, compounds L3, L5, and L6 exhibited positive values against blood-brain barrier (BBB) permeation and receptor activation, Although each compound was a permeability glycoprotein (P-gp) substrate, indicating potential for greater bioavailability. The bioavailability score (BS) of the best-performing compounds were determined at 0.55. However, compounds L1 and L2 displayed more than two violations of Lipinski’s, Veber's, Ghose's, Egan's, and Muegge's rules, indicating limitations in their bioactive functionality as effective drugs. In summary, while all 8 candidates demonstrated more affinity for binding than the co-crystal ligand PF6, certain compounds exhibited unfavorable pharmacokinetic parameters, limiting their progression to clinical phases. Consequently, only 6 compounds (excluding L1 and L2) exhibited both good affinity towards the PDE10A receptor (3HQY) and a favorable ADME profile. Table 4 Pharmacokinetics, physicochemical and pharmacological characteristics of certain derivatives of PDE10A Physicochemical Properties LIPO solubility PHARMACOKINETICS Drug likeness (violations) S/N MW ROT TPSA CLog P Silicos-IT class GI absorpt BBB permeant Pgp substrate Lipinski Ghose Veber Egan Muegge Synthetic Accessibility L1 507.63 8 65.3 5.07 Poorly soluble High No Yes 1 3 0 0 1 4.16 L2 490.6 7 60.37 5.35 Insoluble High No Yes 1 3 0 0 1 3.97 L3 367.45 4 48.01 3.63 Poorly soluble High Yes Yes 0 0 0 0 0 3.21 L4 446.56 6 98.75 4.59 Poorly soluble High No Yes 0 1 0 0 1 4.08 L5 367.45 4 48.01 3.73 Poorly soluble High Yes Yes 0 0 0 0 0 3.19 L6 367.45 4 48.01 3.66 Poorly soluble High Yes Yes 0 0 0 0 0 3.18 L7 415.49 6 86.3 3.47 Poorly soluble High No Yes 0 0 0 0 0 3.3 L8 432.54 6 98.75 4.28 Poorly soluble High No Yes 0 0 0 0 1 3.51 PF6 392.45 6 63.69 4.17 Insoluble High Yes Yes 0 0 0 0 0 3.13 3.5 Toxicity characteristics of the eight selected compounds The predictor of toxicity risk identified potential toxicity hazard associated with fragments of the molecules, indicating potential dangers in the specified risk categories. Studies on toxicity prediction were conducted for PF6 and 6 selected compounds using the pkCSM web tool. Here are the findings based on the 16 most relevant parameters, presented in Table 5 . The AMES toxicity test results indicated positive outcomes for all compounds except for compound L4, which showed a negative result, suggesting non-mutagenicity and lower carcinogenic potential. The maximum recommended tolerated dose (MRTD) was found to be very low for compounds L4, L7, and L8, while it was higher regarding the remaining compounds. Predictions regarding development of long QT syndrome, typically associated with potassium channel blockade encoded by hERG, indicated no likelihood of hERG I inhibition for any compound except L7, whereas all compounds exhibited potential for hERG II inhibition. Only compounds L3 and L6 demonstrated negative hepatoxicity, indicative of support for normal liver function. None of the compounds caused skin sensitization. Alerts were raised for various structural alerts such as non-genotoxic carcinogenicity, acute oral toxicity, Medchem unfriendly status, non-biodegradability, and toxicity to aquatic organisms. Compounds L3, L5, and L6 showed alerts for non-genotoxic carcinogenicity, while LD50_oral of all compounds did not raise any alerts. Compounds L4 and L8 triggered alerts for surechembl, while all compounds raised alerts for non-biodegradability. No compounds, including PF6, raised alerts for acute aquatic toxicity. The webtool ADMET Lab 2.0 ( https://admetmesh.scbdd.com/ ) was utilized to assess the environmental toxicity of the selected compounds. This involved predicting environmental toxicity using parameters such as the 96-hour fathead minnow 50% fatal concentration, 48-hour Daphnia magna 50% lethal concentration, Tetrahymena pyriformis 50% growth inhibition concentration, and bioconcentration parameters. Table 5 presents the results. In all cases, the compounds were found to fit within the application area of the models, indicating that they are applicable for environmental toxicity prediction. Table 5 provides detailed outcomes of the environmental toxicity predictions obtained from ADMET Lab 2.0. Table 5 Results of six compounds' screening for toxicity: PDE10A inhibition Compound L3 L4 L5 L6 L7 L8 PF6 AMES toxicity Yes No Yes Yes Yes Yes Yes Max. tolerated dose (Human) 0.444 0.234 0.554 0.445 0.363 0.22 0.698 hERG I inhibitor No No No No Yes No No hERG II inhibitor Yes Yes Yes Yes Yes Yes Yes Hepatotoxicity No Yes Yes No Yes Yes Yes Skin Sensitisation No No No No No No No Minnow toxicity -1.563 -1.159 -0.628 -1.849 -0.945 -1.337 0.087 NonGenotoxic_ Carcinogenicity 1 0 1 1 0 0 0 LD50_oral 0 0 0 0 0 0 0 SureChEMBL 0 1 0 0 0 1 0 Non -Biodegradable 1 1 1 1 1 1 1 Acute Aquatic Toxicity 0 0 0 0 0 0 0 BCF 2.181 1.871 1.653 2.154 2.014 2.087 2.691 IGC50 4.154 4.998 4.195 4.203 4.826 4.979 4.863 LC50 4.53 5.885 4.33 4.6 5.578 5.849 6.676 LC50DM 4.491 6.255 5.205 4.566 6.357 6.148 5.486 3.6 Assessment of six selected compounds' bioactivity score and target prediction To evaluate the action of common compounds and drugs, which are commonly evaluated using four primary criteria: their impact on G protein-coupled receptor (GPCR) ligands, modulation of ion channels, inhibition of proteases, kinases, and enzymes, and interaction with nuclear receptor ligands. The Molinspiration Cheminformatics software was used to identify the drug activity of the 6 drug complexes, the findings displayed in Table 6 . A bioactivity score above 0.00 indicates a very likely biological activity, while scores between − 0.50 and 0.00 suggest a modest amount of activity. scores that are lower than − 0.50 indicate inactivity. All compounds demonstrated moderate activity as nuclear receptor ligands. Compounds L4, L7, L8, and PF6 exhibited high biological activity against protease inhibitors, whereas compounds L3, L5, and L6 showed moderate activity. All compounds exhibited high activity against kinase inhibitors, GPCR ligands, ion channel modulators, and enzyme inhibitors. The findings indicate that the physiological effects of the drug complexes might be influenced by multiple pathways. These pathways include interactions with nuclear receptor ligands, kinase inhibitors, ion channel modulators, GPCR ligands, enzyme inhibitors, and protease inhibitors. The bioactivity scores reveal a moderate interaction across all drug targets. To further predict the protein targets and assess potential efficacy, the Swiss Target Prediction web tool was employed. The analysis revealed that complexes L3, L5, L6, and L7 have a higher likelihood of acting on Family A G protein-coupled receptors, whereas complexes L4 and L8 are more likely to target kinase inhibitors. Additionally, all the complexes (L3, L4, L5, L6, L7, and L8) have an equal probability of interacting with phosphodiesterase inhibitors (Fig. 3 ). Table 6 Prediction of six selected compounds' targets Compound GPCR ligand Ion channel modulator Kinase inhibitor Nuclear receptor ligand Protease inhibitor Enzyme inhibitor L3 0.2 0.27 0.02 -0.44 -0.17 0.1 L4 0.18 0.04 0.29 -0.05 0.07 0.12 L5 0.45 0.45 0.43 -0.32 -0.15 0.31 L6 0.47 0.45 0.48 -0.25 -0.01 0.41 L7 0.35 0.11 0.65 -0.26 0.15 0.18 L8 0.13 0.01 0.43 -0.14 0.01 0.07 PF6 0.16 0.08 0.49 -0.12 0.04 0.21 3.7 FMO examination of six selected compounds Quantum mechanical techniques have garnered significant attention in computer-aided drug design, in particular when utilizing quantum mechanical descriptors as the HOMO-LUMO (HL) gap, softness (S), hardness (Ƞ), electronegativity (χ), chemical potential (µ), and electrophilicity index (ω) to calculate pharmacological, ecotoxicological and physicochemical properties. These characteristics shed light on the general reactivity of a molecule. The frontier molecular orbital (FMO) theory suggests that the distribution of orbital frontiers is a useful measure of reactivity. When determining interactions with other species, the HOMO and LUMO energy play important roles. The energy of the Highest Occupied Molecular Orbital (HOMO) reflects a molecule's potential to donate electrons to the low-energy unoccupied molecular orbitals of other molecules. Conversely, the energy of the Lowest Unoccupied Molecular Orbital (LUMO) reflects its potential to accept electrons. A larger HOMO-LUMO (HL) gap indicates both higher kinetic stability and lower chemical reactivity, suggesting enhanced chemical stability and reduced reactivity. In our study, DFT analysis revealed that all 6 compounds exhibited low energy gaps compared to reference PF6, with L5 showing the lowest energy gap, signifying high reactivity and kinetic stability (Fig. 4 ). The compound L6 had the greatest chemical potential (µ), signifying a tendency for electrons to escape. While softness (S) gauges reactivity, chemical hardness (Ƞ) is directly related to stability. From (Table 7 ) Compound L7 showed the least amount of softness and highest chemical hardness, indicating little intramolecular charge transfer. Compound L5, on the other hand, had the highest softness and lowest chemical hardness values. Fundamentally, electronegativity (χ) is a molecule's propensity for attracting electrons. In our investigation, we found greater electronegativity levels between 2.9 and 3.7 eV. A useful tool for understanding electron transport and stability in drug complexes is the electrophilicity index. Higher electrophilicity index values indicate highly reactive molecules. Compounds L3, L5, and L6 showed higher values compared to PF6, indicating good electrophilic behavior, while L4, L7, and L8 displayed good nucleophilic behavior. Overall, most compounds were comparable to reference PF6, exhibiting an appropriate binding affinity, which is essential to getting superior biological activities. Table 7 Global reactivity characteristics of six PDE 10A-inhibiting compounds S/N E H (eV) E L (eV) E gap µ(eV) Ƞ(eV) S(eV − 1 ) χ(eV) ω(eV) L3 -5.587 -1.751 3.836 -3.669 1.918 0.521 3.669 3.508 L4 -5.575 -0.986 4.588 -3.280 2.294 0.436 3.280 2.345 L5 -5.524 -1.827 3.697 -3.676 1.849 0.541 3.676 3.654 L6 -5.607 -1.795 3.812 -3.701 1.906 0.525 3.701 3.593 L7 -5.323 -0.626 4.698 -2.974 2.349 0.426 2.974 1.883 L8 -5.566 -0.990 4.576 -3.278 2.288 0.437 3.278 2.348 PF6 -8.219 -1.442 6.777 -4.830 3.388 0.295 4.830 3.443 3.8 MEP examination of six selected compounds By displaying a molecule's charge distribution, the molecular electrostatic potential (MEP) surface sheds light on its physical and chemical characteristics. It helps to locate the molecule's active areas that are nucleophilic and electrophilic. A positive electrostatic potential (ESP) is produced when a region is designated for a point charge with a greater positive charge because of a repulsive interaction with the ligand. On the other hand, an attractive contact results in a negative ESP whether there is an excess of negative charge where the point charge is located. Figure 5 shows the MEP maps of the selected compounds. Red denotes the nucleophilic zone, blue the electrophilic zone, and intermediate hues the halfway MEP values. For PF6, the values of its electrophilic potential were (-0.2161) and (+ 0.0994) a.u., respectively. H atoms and alkyl groups often have positive charges, whereas O and N atoms typically carry negative charges. The compounds with the highest negative values were L6 and L3, which showed equivalent values of (-0.1884) and (-0.1891) a.u., respectively. The majority of the time, compounds L7, L4, and L8 showed positive values of (+ 0.1483), (+ 0.1473), and (+ 0.1448) a.u., respectively. Conclusion The study focused on computational analysis of derivatives including pyrazine, quinazoline, triazine, hydrazone, and cinnoline. Initially, 8 compounds underwent molecular docking to assess their binding affinity and interactions with amino acids associated with inhibitory activity against PDE10A protein (PDB ID 3HQY), compared to a co-crystal ligand. Subsequently, 6 compounds exhibited favorable ADMET properties, bioactivity scores, and target predictions, indicating significant potential for biological activity. Utilizing conceptual DFT reactivity parameters in quantum-chemistry analysis, it was revealed that these compounds possess comparable electrophilic/nucleophilic strength to PF6 and resist electron changes throughout the molecule. Additionally, MEP calculation identified new favored sites for bond formation, potentially explaining the candidates' increased inhibitory activity. Overall, the in-silico investigation suggests that these 6 analogues hold promise as potential drugs for treating schizophrenia. AI-based (QSAR) advancements are anticipated to lead to more accurate, efficient, and financially feasible methods for compound screening, expediting drug creation, reducing environmental risks, and improving materials science. Declarations Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request Acknowledgments We thank to Department of Chemistry, B B A University for providing computational studies. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author information Authors and Affiliations Department of Chemistry, Babasaheb Bhimrao Ambedkar University (A Central University) Lucknow, Uttar Pradesh, 226025, India Ashutosh Kharwar & Anjani Kumar Tiwari Author Contributions All the authors read and approved the final manuscript. AK: writing, designing, visualization; AKT: formal analysis and supervisor Corresponding author Correspondence to Anjani Kumar Tiwari. Ethics declarations Ethical approval None to report. Disclosure statement Conflict of Interest The authors declare that there are no conflicts of interest. Compliance with Ethical Standards This article does not contain any studies involving human or animal subjects. References Al-Nema M, Gaurav A, Akowuah G (2018) Discovery of natural product inhibitors of phosphodiesterase 10A as novel therapeutic drug for schizophrenia using a multistep virtual screening. 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Journal of Molecular Biology 261: 470–489. https://doi.org/10.1006/jmbi.1996.0477 Warren GL, Andrews CW, Capelli A-M, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A Critical Assessment of Docking Programs and Scoring Functions. Journal of Medicinal Chemistry 49: 5912–5931. https://doi.org/10.1021/jm050362n Gastreich M, Lilienthal M, Briem H, Claussen H (2006) Ultrafast de novo docking combining pharmacophores and combinatorics. Journal of Computer-Aided Molecular Design 20: 717–734. https://doi.org/10.1007/s10822-006-9091-x Chadha N, Singh D, Milton MD, Mishra G, Daniel J, Mishra AK, Tiwari AK (2020) Computational prediction of interaction and pharmacokinetics profile study for polyamino-polycarboxylic ligands on binding with human serum albumin. 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2","display":"","copyAsset":false,"role":"figure","size":5203495,"visible":true,"origin":"","legend":"\u003cp\u003eSelected PDE10A derivatives in 2D and 3D images\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6532706/v1/f12c6af85834117327c4053d.png"},{"id":82061233,"identity":"6f416f09-98dd-4bd6-bbd7-0a924960e599","added_by":"auto","created_at":"2025-05-06 11:40:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":784871,"visible":true,"origin":"","legend":"\u003cp\u003eTarget prediction of the six compounds.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6532706/v1/2dabe17064f609a1faedbd00.png"},{"id":82060419,"identity":"50b475e7-2c4a-4514-8d9f-701643246687","added_by":"auto","created_at":"2025-05-06 11:32:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2065518,"visible":true,"origin":"","legend":"\u003cp\u003eMolecule orbitals of 6 active compounds and the reference drug PF6\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6532706/v1/15110cdf35a4b423bd4f2019.png"},{"id":82060422,"identity":"7c3a1c7d-8a3b-4cc7-9fe0-739de786e22a","added_by":"auto","created_at":"2025-05-06 11:32:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1174713,"visible":true,"origin":"","legend":"\u003cp\u003eMEP plots with scale range (a.u.) for 6 active compounds and the reference drug PF6\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6532706/v1/3e27f05ea1140ecd19d2e22c.png"},{"id":82062751,"identity":"8180cd9f-8aed-47a6-978b-a42b96bb156f","added_by":"auto","created_at":"2025-05-06 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Introduction","content":"\u003cp\u003eA significant unmet medical need is schizophrenia, a mental illness. Schizophrenia affects an estimated 0.3\u0026ndash;0.7% of the population, and its social consequences are significant[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A single adverse effects including weight gain, hyperglycemia, dyslipidemia, and extrapyramidal symptoms have been related to the pharmacology of atypical antipsychotic drugs, which block the dopamine and serotonin receptors.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These medications are the mainstay of contemporary schizophrenia treatment. The cognitive impairments associated with schizophrenia have also been linked to dysregulation of the glutamate system[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The use of phosphodiesterase 10A (PDE10A) inhibitors to treat schizophrenia has gained popularity recently. PDE10A is a dual substrate phosphodiesterase that is widely expressed in the striatal region of the brain. It hydrolyses two second messengers, cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Because PDE10A operates downstream of dopamine and glutamate receptors within their signalling pathways, and given that striatal dysfunction is linked to the pathophysiology of schizophrenia, directly inhibiting PDE10A could elevate cGMP and cAMP levels in the striatum. This may improve cognitive deficits and behavioural control in schizophrenia without the adverse side effects associated with current standard treatments[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Eleven PDE families make up the PDE superfamily, which generates more than fifty functionally distinct enzymes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nonetheless, there is a 25\u0026ndash;35% amino acid similarity among PDE families, which makes the creation of specific PDE10A inhibitors a possible objective[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, it has been proposed that blocking PDE10A offers a new way to treat Alzheimer's by counteracting the negative effects of amyloid-β peptide on the cAMP-response element-binding protein (CREB) pathway, a crucial regulator of the formation of long-term memory[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The entire process of drug development has been impacted through computational drug design and discovery approaches developed over the past 20 years [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In order to comprehend the molecular basis of PDEs inhibition, computational chemistry was first applied in the 1980s[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The first study to be published made an effort to describe the pharmacophore for PDE inhibitors by attempting to explain the association between particular physicochemical qualities and efficacy of existing inhibitors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The goal of medicinal chemists has been to create strong PDE10A inhibitors using structure-based and ligand-based methods, aiming to minimize side effects[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Despite these positive developments, scientists are still having difficulty developing drugs that work. In silico modelling techniques, a fruitful and economical technology in the design of novel lead compounds should be employed in conjunction with experimental procedures to streamline the drug discovery process[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the pursuit of designing a novel molecule targeting Schizophrenia, specifically focusing on PDE10A derivatives, we have assembled a collection of 100 distinct compounds. These compounds feature monocyclic heterocyclic rings, drawing from derivatives of pyrazine, quinazoline, triazine, hydrazone, cinnoline, and other promising candidates identified in preclinical models of schizophrenia[\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Computational studies were then conducted across five stages to streamline the selection process and identify the most promising molecules. Initially, molecular docking was performed to assess the binding affinity of the compounds, with screening based on Hyde scores. Subsequent stages involved evaluations of physicochemical properties, pharmacokinetics, toxicity, target prediction, and drug-likeness with a focus on identifying the most potent candidates within the parameters set by relevant software tools. Lastly, computations based on quantum mechanics were used to examine the chosen drug compounds' stability, reactivity and electronic characteristics. Parameters consisting of HOMO-LUMO gaps in energy, softness, chemical potential, hardness, electrophilicity index and electronegativity were examined in this context. This comprehensive study aims to investigate the inhibitory activities of the identified compounds using in silico research, with the ultimate goal of developing PDE10A inhibitors as potential candidates for Schizophrenia treatment. The screening process yielded a PDE10A fragment showing potent and selective inhibitory activity, demonstrating effectiveness in a preclinical test for schizophrenia.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003ch2\u003e2.1 Docking validation, protein preparation, and ligand preparation\u003c/h2\u003e\n\u003cp\u003eA series of Hundred (100) molecules of pyrazine, quinazoline, triazine, hydrazone and cinnoline derivatives were extracted from the published article. Using the Chemdraw program, the 2D structures of 100 molecules were created. After that, optimization was performed using the force field (MMFF) with Chem3D software in accordance with our earlier research[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe PDE10A protein\u0026apos;s X-ray crystal structure (PDB ID: 3HQY) was obtained from the protein data bank website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org\u003c/span\u003e\u003c/span\u003e). It made it possible to identify certain proteins\u0026apos; locations within the human body. LeadIT Version 2.3.2 (BioSolveIT, Germany, 2018) was used to integrate the crystal structure of the PDE10A protein after sufficient XRD data, R-value, amino acid residues, chain number and resolution were achieved. Next, \u0026quot;Prepare receptor\u0026quot; was used to prepare the structure. In summary, the original PDB structure was processed by selecting both chains A, and four binding pockets of amino acids were used to carry out an additional automatic detection method.\u003c/p\u003e\n\u003cp\u003ePrior to docking the molecular dataset, the redocking procedure validated the use of LeadIT. Using the redocking procedure, the ligand bound from the X-ray crystal structure was docked back into the binding pocket of the enzyme.[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. This was done to ensure that LeadIT could replicate the orientation and placement of the inhibitors as they were shown in the X-ray structure. [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003ch2\u003e2.2 Molecular Docking\u003c/h2\u003e\n\u003cp\u003eLeadIT, part of the BioSolveIT Package, facilitated docking studies of custom-designed ligands within target receptors, maintaining a 6.5 spacing among amino acid residues. Following docking, potential ligand-protein interactions were explored using HYDE assessment.[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Docking calculations adhered to software instructions, generating ten poses, each with a binding affinity for the PDE10A site measured in kJ/mol. Docking scores, L.E. values and estimated affinities from HYDE score function were assessed to select optimal poses[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAccording to the logP atomic increment system, the function of empirical scoring of HYDE (Eq. 1) uses terms for hydrogen bonding, hydration and atom-specific desolvation (i.e., without weighting factors). The amount of non-hydrogen atoms in the molecule and the quotient of ∆G (Eq. 2) were used to calculate the ligand binding affinity.\u003c/p\u003e\n\u003cp\u003e∆G\u003csub\u003eHyde\u003c/sub\u003e=\u0026sum;\u003csub\u003eatom i\u003c/sub\u003e [∆G\u003csup\u003ei\u003c/sup\u003e\u003csub\u003eDehydration\u003c/sub\u003e+∆G\u003csup\u003ei\u003c/sup\u003e\u003csub\u003eH\u0026minus;bonds\u003c/sub\u003e] \u0026hellip;\u0026hellip; (1)\u003c/p\u003e\n\u003cp\u003eLE=∆G ∕ N \u0026hellip;\u0026hellip; (2)\u003c/p\u003e\n\u003cp\u003eWhere ∆G = -RTlnK\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;number of non-hydrogen atoms\u003c/p\u003e\n\u003cp\u003eThe visualization was carried out via the Discovery Studio Visualizer program[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003ch2\u003e2.3 Predicting toxicity, drug likeness and pharmacokinetics\u003c/h2\u003e\n\u003cp\u003ePotential drug candidates were found through the use of the Swissadme online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.swissadme.ch\u003c/span\u003e\u003c/span\u003e), which is used to predict various pharmacokinetic, physicochemical parameters, and drug-likeness characteristics of compounds in silico. pkCSM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/pkcsm\u003c/span\u003e\u003c/span\u003e) and ADMET lab 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com/\u003c/span\u003e\u003c/span\u003e) were used to measure a variety of toxicity parameters[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003ch2\u003e2.4 Bioactivity score\u003c/h2\u003e\n\u003cp\u003eThe Molinspiration online software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.molinspiration.com\u003c/span\u003e\u003c/span\u003e) was utilized to forecast the bioactivity of the acids that were being studied. G protein-coupled receptors (GPCR ligands), ion channel modulators, nuclear receptor ligands, kinase inhibitors, protease inhibitors, and enzyme inhibitors were among the targets for which activity scores were computed. The online SwissTarget-prediction webtool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003c/span\u003e) was also utilized to obtain an accurate protein target prediction.[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\n\u003ch2\u003e2.5 Evaluation of Chemical Reactivity using DFT\u003c/h2\u003e\n\u003cp\u003eBy analyzing the electrical characteristics of the six compounds, the density functional theory has been used to show that our target molecules are chemically stable. Using the 6-31G + (d, p) basis set and the B3LYP hybrid functional, the Gaussian 09 software program carried out the DFT calculations. GaussView was used to display the results. Electronic parameters in the gaseous state, such as the molecular electrostatic potential, quantum chemical reactivity descriptors, HOMO-LUMO band gap, LUMO power value (ELUMO), and energy value of the HOMO (EHOMO), were calculated based on the optimized structure. The following formulas were used to determine the quantum chemical reactivity descriptors, such as softness (S), electronegativity (\u0026chi;), chemical potential (\u0026micro;), hardness (Ƞ) and electrophilicity index (\u0026omega;)[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Protocol validation for docking\u003c/h2\u003e\n \u003cp\u003eResolution, sequence length, R-value, and Hyde score values are among the characteristics of PDE10A receptors shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The protein superimposition between the observed X-ray crystallographic structure posture and the docked molecule pose, as seen in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The root mean square deviation (RMSD) contrasting these two positions is measured at 1.1 \u0026Aring;. The compound\u0026apos;s estimated RMSD values align closely with this measurement, with a value of 1.1 \u0026Aring;, which falls below the threshold of 2 \u0026Aring;. This confirms the accuracy and reliability of the method employed in this study.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProtein (3HQY) properties, Hyde score and RMSD detail\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnzymes ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAmino acids number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution (\u0026Aring;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCo-crystallized ligand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHyde score (KJ/Mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSD (\u0026Aring;)\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\u003e3HQY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Docking analysis of screened compounds\u003c/h2\u003e\n \u003cp\u003eMolecular docking serves as a valuable modeling technique utilized to evaluate the binding efficacy of drugs to target receptors. Central to this process is the assessment of affinity, or \u0026quot;scoring,\u0026quot; which involves gauging the ligand\u0026apos;s interaction within the binding pocket. In our approach, ligand-binding affinity is predicted using the HYDE technique, which also computes the realistic free energies involved in ligand-protein binding. A higher negative value denotes a higher ligand-protein complex binding affinity. We selected 100 previously reported compounds for binding assessment against PDE10A inhibitors using PDB ID: 3HQY for docking, with PF6 serving as the co-crystallized ligand. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e lists the various docking analysis sub-parameters along with the renamed selected compounds. Notably, compounds L1 to L8 exhibited superior binding affinity compared to the co-crystallized ligand PF6. In terms of its binding affinity per heavy atom, ligand potency is quantified by ligand efficiency (L.E.) Higher (or close to reference) ligand efficiency values for all compounds indicate more efficient ligands, except for L1 and L2. The lipophilic contact area of a protein represents the degree of lipophilic interactions between its hydrophobic regions and ligand. All ligands exhibited higher lipophilic contact area values compared to the reference PF6. The interaction within lipophilic and hydrophilic areas is quantified by the ambiguous score. Higher values of the ambiguous score for all compounds indicate a enhanced capacity to balance interactions in both kinds of scenarios. Compounds L2, L3, L5, L6, and L7 exhibited high Clash penalty scores, suggesting unfavorable steric interactions that could potentially impact binding.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePDE 10A derivatives docking results involving PDB ID: 3HQY\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\u003eRenaming\u003c/p\u003e\n \u003cp\u003eof compound\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH.S.\u003c/p\u003e\n \u003cp\u003e(KJ/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLIPO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMBIG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCLASH\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\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.1720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.8751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-19.1865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.1879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-60\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\u003e-19.4875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.8961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-17.5705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.6344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-59\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\u003e-18.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.4303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-59\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\u003e-19.5586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-59\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\u003e-18.2129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.7909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.0178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-59\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\u003e-17.8514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.5954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-58\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\u003e-18.2079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.2058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Binding interaction and interpretation of selected compounds\u003c/h2\u003e\n \u003cp\u003eThe two-dimensional (2D) interactions between protein-ligand complexes were analyzed using Discovery Studio software, with findings summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. These visuals provide insights into binding affinities and interactions that are non-bonding. Hydrogen bonds, pi-pi bonds, and van der Waals forces were identified as the significant interactions between PDE10A and the selected ligands. These interactions play crucial roles in figuring out the drug-receptor interface\u0026apos;s binding affinity and eventually affect the efficacy of the medicine by influencing the stability of the ligands at the target sites. Specifically, compounds L2 to L8, and the reference ligand demonstrated hydrogen bonding with amino acid residues such as serine, glutamine, glycine, and tyrosine. Notably, compound L1 did not exhibit interactions with tyrosine residues. Additionally, interactions involving water molecules and hydrophobic regions, indicative of poor water solubility, were observed. These interactions are characterized by hydrophobic interactions within the protein structure. These findings provide valuable insights into the molecular interactions between the selected compounds and PDE10A, contributing to our understanding of their potential as effective drug candidates. Pi-pi bonds, a common hydrophobic interaction, have been found in L1, L2, L3, L4, L5, L6, L8 including reference PF6 is PHE-719 and GLU-711 except compound L7. Another common hydrophobic interaction with PHE phenylamine and PRO proline which are found in all compounds and standard PF6 (except PHE-719 is absent in L7 and PRO-702 absent in L1). Each compound exhibits an increased quantity of residues engaged in Van der Waals interactions compared to the standard PF6, with the exception of L1, which only interacts with two residues: ASP-664 and GLY-715.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of interactions between PDE 10A derivatives and PDB ID: 3HQY\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\u003eH-BOND\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHBI (Pi-Pi bond)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVan der Waals\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\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSER-667 GLN-716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE-686 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASP-664 GLY-715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-683 GLN-716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE-686 PRO-702 GLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEU-625 LEU-665 SER-667 VAL-668 TRP-687 LYS-708 ARG-709 GLU-711 VAL-712 GLY-715 VAL-723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-683 GLN-716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO-702 GLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEU-625 LEU-665 SER-667 THR\u003csup\u003e[k]\u003c/sup\u003e-675 PHE-686 TRP\u003csup\u003e[l]\u003c/sup\u003e-687 LYS\u003csup\u003e[m]\u003c/sup\u003e-708 ARG\u003csup\u003e[n]\u003c/sup\u003e-709 GLY-715 TYR-720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS-515 TYR-683 GLY-715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE-686 PRO-702 GLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMG-2 HIS\u003csup\u003e[o]\u003c/sup\u003e-557 SER-561 LEU-625 LEU-665 SER-667 VAL\u003csup\u003e[p]\u003c/sup\u003e-668 LYS-708 ARG-709 VAL-712 GLN-716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-683 GLN-716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEU-625 LEU-665 SER-667 THR-675 PHE-686 TRP-687 LYS-708 ARG-709 VAL-712 GLY-715 TYR-720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-683 GLN-716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO-702 GLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEU-625 LEU-665 SER-667 THR-675 PHE-686 LYS-708 ARG-709 VAL-712 GLY-715 TYR-720 TRP-752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-683 GLY-715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO-702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS-515 HIS-519 CYS\u003csup\u003e[q]\u003c/sup\u003e-666 VAL-668 THR-675 PHE-686 TRP-687 LYS-708 ARG-709 GLU-711 TYR-720 TRP-752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIS-515 TYR-683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO-702 GLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE-625 LEU-665 SER-667 PHE-686 LYS-708 ARG-709 GLY-715 GLN-716 TYR-720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-683 SER-667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE-686 PRO-702 GLU-711 PHE-719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR-514 HIS-515 LEU-625 ASP-664 ALA-679 LYS-708 ARG-709 VAL-712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Physicochemical, drug-likeness and pharmacokinetics assessment of screened compounds\u003c/h2\u003e\n \u003cp\u003eThe Swissadme online web tool was used to evaluate the physicochemical, drug-likeness, and pharmacokinetic features of the top 8 compounds that were chosen. 48 descriptions that cover both physiologically and pharmaceutically significant parameters are included in this web tool. We identified 14 criteria, detailed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, that indicate appropriate ranges for drug-like molecules. Observations on the physicochemical properties revealed that the molecular weight (MW) of compounds L2 to L8 falls within the range of 150\u0026ndash;450 g/mol, with all compounds exhibiting a flexibility (rotatable bonds) of no more than nine. TPSA of our compounds ranged among 20\u0026ndash;130 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e, indicating oral bioavailability. The consensus log Po/w value, a measure of lipophilicity, ranged from \u0026minus;\u0026thinsp;0.7 to +\u0026thinsp;5.0 for all compounds, except L1 and L2, suggesting potential for efficient translocation across biomembranes. L2 and PF6 were insoluble, although the SILICOS-IT model predicted that compounds L1, L3, L4, L5, L6, L7, and L8 would have poor solubility. Evaluation of gastrointestinal (GI) absorption indicated high values for all compounds, suggesting drug-like properties. Additionally, compounds L3, L5, and L6 exhibited positive values against blood-brain barrier (BBB) permeation and receptor activation, Although each compound was a permeability glycoprotein (P-gp) substrate, indicating potential for greater bioavailability. The bioavailability score (BS) of the best-performing compounds were determined at 0.55. However, compounds L1 and L2 displayed more than two violations of Lipinski\u0026rsquo;s, Veber\u0026apos;s, Ghose\u0026apos;s, Egan\u0026apos;s, and Muegge\u0026apos;s rules, indicating limitations in their bioactive functionality as effective drugs. In summary, while all 8 candidates demonstrated more affinity for binding than the co-crystal ligand PF6, certain compounds exhibited unfavorable pharmacokinetic parameters, limiting their progression to clinical phases. Consequently, only 6 compounds (excluding L1 and L2) exhibited both good affinity towards the PDE10A receptor (3HQY) and a favorable ADME profile.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePharmacokinetics, physicochemical and pharmacological characteristics of certain derivatives of PDE10A\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePhysicochemical Properties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLIPO\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esolubility\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePHARMACOKINETICS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eDrug likeness (violations)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS/N\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\u003eROT\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\" colspan=\"2\"\u003e\n \u003cp\u003eCLog P\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSilicos-IT class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGI absorpt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBBB permeant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePgp substrate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLipinski\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGhose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVeber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEgan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMuegge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSynthetic Accessibility\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e507.63\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e65.3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoorly soluble\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4.16\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\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e490.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsoluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e367.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorly soluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e446.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorly soluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e367.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorly soluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e367.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e48.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorly soluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e415.49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorly soluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e432.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoorly soluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.51\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePF6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e392.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsoluble\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Toxicity characteristics of the eight selected compounds\u003c/h2\u003e\n \u003cp\u003eThe predictor of toxicity risk identified potential toxicity hazard associated with fragments of the molecules, indicating potential dangers in the specified risk categories. Studies on toxicity prediction were conducted for PF6 and 6 selected compounds using the pkCSM web tool. Here are the findings based on the 16 most relevant parameters, presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The AMES toxicity test results indicated positive outcomes for all compounds except for compound L4, which showed a negative result, suggesting non-mutagenicity and lower carcinogenic potential. The maximum recommended tolerated dose (MRTD) was found to be very low for compounds L4, L7, and L8, while it was higher regarding the remaining compounds. Predictions regarding development of long QT syndrome, typically associated with potassium channel blockade encoded by hERG, indicated no likelihood of hERG I inhibition for any compound except L7, whereas all compounds exhibited potential for hERG II inhibition. Only compounds L3 and L6 demonstrated negative hepatoxicity, indicative of support for normal liver function. None of the compounds caused skin sensitization. Alerts were raised for various structural alerts such as non-genotoxic carcinogenicity, acute oral toxicity, Medchem unfriendly status, non-biodegradability, and toxicity to aquatic organisms. Compounds L3, L5, and L6 showed alerts for non-genotoxic carcinogenicity, while LD50_oral of all compounds did not raise any alerts. Compounds L4 and L8 triggered alerts for surechembl, while all compounds raised alerts for non-biodegradability. No compounds, including PF6, raised alerts for acute aquatic toxicity.\u003c/p\u003e\n \u003cp\u003eThe webtool ADMET Lab 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://admetmesh.scbdd.com/\u003c/span\u003e\u003c/span\u003e) was utilized to assess the environmental toxicity of the selected compounds. This involved predicting environmental toxicity using parameters such as the 96-hour fathead minnow 50% fatal concentration, 48-hour Daphnia magna 50% lethal concentration, Tetrahymena pyriformis 50% growth inhibition concentration, and bioconcentration parameters. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results.\u003c/p\u003e\n \u003cp\u003eIn all cases, the compounds were found to fit within the application area of the models, indicating that they are applicable for environmental toxicity prediction. Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e provides detailed outcomes of the environmental toxicity predictions obtained from ADMET Lab 2.0.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of six compounds\u0026apos; screening for toxicity: PDE10A inhibition\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\u003eL3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eL8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePF6\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\u003eAMES toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax. tolerated dose\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Human)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehERG I inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ehERG II inhibitor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatotoxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkin Sensitisation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinnow toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonGenotoxic_\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCarcinogenicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD50_oral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSureChEMBL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e-Biodegradable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAquatic Toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBCF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIGC50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLC50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLC50DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Assessment of six selected compounds\u0026apos; bioactivity score and target prediction\u003c/h2\u003e\n \u003cp\u003eTo evaluate the action of common compounds and drugs, which are commonly evaluated using four primary criteria: their impact on G protein-coupled receptor (GPCR) ligands, modulation of ion channels, inhibition of proteases, kinases, and enzymes, and interaction with nuclear receptor ligands. The Molinspiration Cheminformatics software was used to identify the drug activity of the 6 drug complexes, the findings displayed in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. A bioactivity score above 0.00 indicates a very likely biological activity, while scores between \u0026minus;\u0026thinsp;0.50 and 0.00 suggest a modest amount of activity. scores that are lower than \u0026minus;\u0026thinsp;0.50 indicate inactivity. All compounds demonstrated moderate activity as nuclear receptor ligands. Compounds L4, L7, L8, and PF6 exhibited high biological activity against protease inhibitors, whereas compounds L3, L5, and L6 showed moderate activity. All compounds exhibited high activity against kinase inhibitors, GPCR ligands, ion channel modulators, and enzyme inhibitors. The findings indicate that the physiological effects of the drug complexes might be influenced by multiple pathways. These pathways include interactions with nuclear receptor ligands, kinase inhibitors, ion channel modulators, GPCR ligands, enzyme inhibitors, and protease inhibitors. The bioactivity scores reveal a moderate interaction across all drug targets. To further predict the protein targets and assess potential efficacy, the Swiss Target Prediction web tool was employed. The analysis revealed that complexes L3, L5, L6, and L7 have a higher likelihood of acting on Family A G protein-coupled receptors, whereas complexes L4 and L8 are more likely to target kinase inhibitors. Additionally, all the complexes (L3, L4, L5, L6, L7, and L8) have an equal probability of interacting with phosphodiesterase inhibitors (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrediction of six selected compounds\u0026apos; targets\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\u003eGPCR ligand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIon channel modulator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKinase inhibitor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNuclear receptor ligand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtease inhibitor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnzyme inhibitor\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\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePF6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 FMO examination of six selected compounds\u003c/h2\u003e\n \u003cp\u003eQuantum mechanical techniques have garnered significant attention in computer-aided drug design, in particular when utilizing quantum mechanical descriptors as the HOMO-LUMO (HL) gap, softness (S), hardness (Ƞ), electronegativity (\u0026chi;), chemical potential (\u0026micro;), and electrophilicity index (\u0026omega;) to calculate pharmacological, ecotoxicological and physicochemical properties. These characteristics shed light on the general reactivity of a molecule. The frontier molecular orbital (FMO) theory suggests that the distribution of orbital frontiers is a useful measure of reactivity.\u003c/p\u003e\n \u003cp\u003eWhen determining interactions with other species, the HOMO and LUMO energy play important roles. The energy of the Highest Occupied Molecular Orbital (HOMO) reflects a molecule\u0026apos;s potential to donate electrons to the low-energy unoccupied molecular orbitals of other molecules. Conversely, the energy of the Lowest Unoccupied Molecular Orbital (LUMO) reflects its potential to accept electrons. A larger HOMO-LUMO (HL) gap indicates both higher kinetic stability and lower chemical reactivity, suggesting enhanced chemical stability and reduced reactivity. In our study, DFT analysis revealed that all 6 compounds exhibited low energy gaps compared to reference PF6, with L5 showing the lowest energy gap, signifying high reactivity and kinetic stability (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The compound L6 had the greatest chemical potential (\u0026micro;), signifying a tendency for electrons to escape. While softness (S) gauges reactivity, chemical hardness (Ƞ) is directly related to stability. From (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) Compound L7 showed the least amount of softness and highest chemical hardness, indicating little intramolecular charge transfer. Compound L5, on the other hand, had the highest softness and lowest chemical hardness values. Fundamentally, electronegativity (\u0026chi;) is a molecule\u0026apos;s propensity for attracting electrons. In our investigation, we found greater electronegativity levels between 2.9 and 3.7 eV. A useful tool for understanding electron transport and stability in drug complexes is the electrophilicity index. Higher electrophilicity index values indicate highly reactive molecules. Compounds L3, L5, and L6 showed higher values compared to PF6, indicating good electrophilic behavior, while L4, L7, and L8 displayed good nucleophilic behavior.\u003c/p\u003e\n \u003cp\u003eOverall, most compounds were comparable to reference PF6, exhibiting an appropriate binding affinity, which is essential to getting superior biological activities.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGlobal reactivity characteristics of six PDE 10A-inhibiting 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\u003eS/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003eH\u003c/sub\u003e(eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003eL\u003c/sub\u003e(eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003egap\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026micro;(eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eȠ(eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS(eV\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;(eV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026omega;(eV)\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\u003eL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePF6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 MEP examination of six selected compounds\u003c/h2\u003e\n \u003cp\u003eBy displaying a molecule\u0026apos;s charge distribution, the molecular electrostatic potential (MEP) surface sheds light on its physical and chemical characteristics. It helps to locate the molecule\u0026apos;s active areas that are nucleophilic and electrophilic. A positive electrostatic potential (ESP) is produced when a region is designated for a point charge with a greater positive charge because of a repulsive interaction with the ligand. On the other hand, an attractive contact results in a negative ESP whether there is an excess of negative charge where the point charge is located. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the MEP maps of the selected compounds. Red denotes the nucleophilic zone, blue the electrophilic zone, and intermediate hues the halfway MEP values. For PF6, the values of its electrophilic potential were (-0.2161) and (+\u0026thinsp;0.0994) a.u., respectively. H atoms and alkyl groups often have positive charges, whereas O and N atoms typically carry negative charges. The compounds with the highest negative values were L6 and L3, which showed equivalent values of (-0.1884) and (-0.1891) a.u., respectively. The majority of the time, compounds L7, L4, and L8 showed positive values of (+\u0026thinsp;0.1483), (+\u0026thinsp;0.1473), and (+\u0026thinsp;0.1448) a.u., respectively.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThe study focused on computational analysis of derivatives including pyrazine, quinazoline, triazine, hydrazone, and cinnoline. Initially, 8 compounds underwent molecular docking to assess their binding affinity and interactions with amino acids associated with inhibitory activity against PDE10A protein (PDB ID 3HQY), compared to a co-crystal ligand. Subsequently, 6 compounds exhibited favorable ADMET properties, bioactivity scores, and target predictions, indicating significant potential for biological activity. Utilizing conceptual DFT reactivity parameters in quantum-chemistry analysis, it was revealed that these compounds possess comparable electrophilic/nucleophilic strength to PF6 and resist electron changes throughout the molecule. Additionally, MEP calculation identified new favored sites for bond formation, potentially explaining the candidates\u0026apos; increased inhibitory activity. Overall, the in-silico investigation suggests that these 6 analogues hold promise as potential drugs for treating schizophrenia. AI-based (QSAR) advancements are anticipated to lead to more accurate, efficient, and financially feasible methods for compound screening, expediting drug creation, reducing environmental risks, and improving materials science.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank to Department of Chemistry, B B A University for providing computational studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Chemistry, Babasaheb Bhimrao Ambedkar University (A Central University) Lucknow, Uttar Pradesh, 226025, India \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAshutosh Kharwar \u0026amp; Anjani Kumar Tiwari\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors read and approved the final manuscript. AK: writing, designing, visualization; AKT: formal analysis and supervisor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Anjani Kumar Tiwari.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies involving human or animal subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Nema M, Gaurav A, Akowuah G (2018) Discovery of natural product inhibitors of phosphodiesterase 10A as novel therapeutic drug for schizophrenia using a multistep virtual screening. 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Arabian Journal of Chemistry 15: 103942. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arabjc.2022.103942\u003c/span\u003e\u003cspan address=\"10.1016/j.arabjc.2022.103942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDekir A, Berredjem M, Rachedi KO, Bahadi R, Djouad SE, Bouacida S, Grib I, Iqbal N, Redjemia R, Boussaker M (2023) X-ray crystallographic study, molecular docking, molecular dynamics and DFT/ADMET analyses of carboxylsulfamides. 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Arabian Journal of Chemistry 15: 103464. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arabjc.2021.103464\u003c/span\u003e\u003cspan address=\"10.1016/j.arabjc.2021.103464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PDE 10A, Molecular docking, ADMET, DFT, MEP","lastPublishedDoi":"10.21203/rs.3.rs-6532706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6532706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecently, interest in phosphodiesterase 10A inhibitors has increased for schizophrenia treatment. Medicinal chemists have extensively worked on developing potent PDE10A inhibitors with minimal side effects. However, despite these efforts, PDE10A inhibitors have yet to gain approval for treating neurodegenerative disorders, possibly due to limited research in this area. In this study, we used an in-silico approach to evaluate 100 novel compounds derived from pyrazine, quinazoline, triazine, hydrazone, and cinnoline for their interaction with the PDE10A receptor (PDB ID: 3HQY) through molecular docking. Based on their drug-like properties, including physicochemical characteristics and ADMET profiles, eight top-ranking compounds, comparable to the standard drug PF6, were selected. We further narrowed this down to six highly promising molecules and identified protein targets for the PDE10A compound using a target prediction tool. Further investigations, including FMO (Frontier Molecular Orbital) and MEP (Molecular Electrostatic Potential) studies, showed increased stability in the drug complexes due to a larger HOMO-LUMO gap. Additionally, a significant electrophilicity index indicated favorable electrophilic behavior and increased reactivity of the drugs. Overall, a detailed examination has identified new favorable sites for bond formation in the 6 anticipated analogs, suggesting their potential drugs for treating schizophrenia diseases.\u003c/p\u003e","manuscriptTitle":"In silico study of PDE10A inhibitors for schizophrenia disease: Molecular docking, ADMET and DFT analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 11:31:57","doi":"10.21203/rs.3.rs-6532706/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-26T12:13:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-22T02:41:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332246826120594536057676695588119406272","date":"2025-05-13T11:36:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T03:20:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T19:10:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99031011549117219038521666005325974012","date":"2025-05-02T18:07:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16768705839884175260891730100125006223","date":"2025-05-01T18:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300067488234918096775137362271176106936","date":"2025-04-30T18:02:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T17:14:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-29T11:32:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T06:55:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-28T06:54:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2025-04-26T05:14:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a373f0e-301b-45ba-bc96-e6060500e4c2","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T11:38:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 11:31:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6532706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6532706","identity":"rs-6532706","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}