QSAR and DFT Studies of Some Tacrine-Hydroxamate Derivatives as Inhibitors of Cholinesterase (AChEs) in the Treatment of Alzheimer's Disease

Introduction: This work was devoted to an in silico investigation conducted on twenty-eight Tacrine-hydroxamate derivatives as a potential treatment for Alzheimer's disease using DFT and QSAR modeling techniques. Method(s): The data set was randomly partitioned into a training set (22 compounds) and a test set (6 compounds). Then, fourteen models were built and were used to compute the predicted pIC50 of compounds belonging to the test set. Result(s): Al built models were individualy validated using both internal and external validation methods, including the Y-Randomization test and Golbraikh and Tropsha's model acceptance criteria. Then, one model was selected for its higher R2, R2test, and Q2cv values (R2 = 0.768, R2adj = 0.713, MSE = 0.304, R2test=0.973, Q2cv = 0.615). From these outcomes, the activity of the studied compounds toward the main protease of Cholinesterase (AChEs) seems to be influenced by 4 descriptors, i.e., the total dipole moment of the molecule (mu), number of rotatable bonds (RB), molecular topology radius (MTR) and molecular topology polar surface area (MTPSA). The effect of these descriptors on the activity was studied, in particular, the increase in the total dipole moment and the topological radius of the molecule and the reduction of the rotatable bond and topology polar surface area increase the activity. Conclusion(s): Some newly designed compounds with higher AChEs inhibitory activity have been designed based on the best-proposed QSAR model. In addition, ADMET pharmacokinetic properties were carried out for the proposed compounds, the toxicity results indicate that 7 molecules are nontoxic.Copyright © 2023 Bentham Science Publishers.

The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation.

Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using 'PaDEL' and 'ChemDes' software, and then redundant and non-significant descriptors were eliminated by a module in 'QSARINS ver. 2.2.2'. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.

QSAR and molecular docking studies on designing potent inhibitors of SARS-CoVs main protease.

Background: Severe acute respiratory syndrome coronavirus (SARS-CoVs) have emerged as a global health threat, which had caused a high rate of mortality. There is an urgent need to find effective drugs against these viruses. Objective: This study aims to predict the activity of unsymmetrical aromatic disulfides by constructing a QSAR model, and to design new compounds according to the structural and physicochemical attributes responsible for higher activity towards SARS-CoVs main protease. Methods: All molecules were constructed in ChemOffice software and molecular descriptors were calculated by CODESSA software. A regression-based linear heuristic method was established by changing descriptors datasets and calculating predicted IC50 values of compounds. Then, some new compounds were designed according to molecular descriptors from the heuristic method model. The compounds with predicted values smaller than a set point were constantly screened out. Finally, the properties analysis and molecular docking were conducted to further understand the structure-activity relationships of these finalized compounds. Results: The heuristic method explored the various descriptors responsible for bioactivity and gained the best linear model with R2 0.87. The success of the model fully passed the testing set validation, proving that the model has both high statistical significance and excellent predictive ability. A total of 5 compounds with ideal predicted IC50 were found from the 96 newly designed derivatives and their properties analyze was carried out. Molecular docking experiments were conducted for the optimal compound 31a, which has the best compound activity with good target protein binding capability. Conclusion: The heuristic method was quite reliable for predicting IC50 values of unsymmetrical aromatic disulfides. The present research provides meaningful guidance for further exploration of the highly active inhibitors for SARS-CoVs.

Structure-activity relationships of dihydropyrimidone inhibitors against native and auto-processed human neutrophil elastase.

BACKGROUND: Human neutrophil elastase (HNE) is a key driver of systemic and cardiopulmonary inflammation. Recent studies have established the existence of a pathologically active auto-processed form of HNE with reduced binding affinity against small molecule inhibitors. METHOD: AutoDock Vina v1.2.0 and Cresset Forge v10 software were used to develop a 3D-QSAR model for a series of 47 DHPI inhibitors. Molecular Dynamics (MD) simulations were carried out using AMBER v18 to study the structure and dynamics of sc (single-chain HNE) and tcHNE (two-chain HNE). MMPBSA binding free energies of the previously reported clinical candidate BAY 85-8501 and the highly active BAY-8040 were calculated with sc and tcHNE. RESULTS: The DHPI inhibitors occupy the S1 and S2 subsites of scHNE. The robust 3D-QSAR model showed acceptable predictive and descriptive capability with regression coefficient of r2 = 0.995 and cross-validation regression coefficient q2 = 0.579 for the training set. The key descriptors of shape, hydrophobics and electrostatics were mapped to the inhibitory activity. In auto-processed tcHNE, the S1 subsite undergoes widening and disruption. All the DHPI inhibitors docked with the broadened S1'-S2' subsites of tcHNE with lower AutoDock binding affinities. The MMPBSA binding free energy of BAY-8040 with tcHNE reduced in comparison with scHNE while the clinical candidate BAY 85-8501 dissociated during MD. Thus, BAY-8040 may have lower inhibitory activity against tcHNE whereas the clinical candidate BAY 85-8501 is likely to be inactive. CONCLUSION: SAR insights gained from this study will aid the future development of inhibitors active against both forms of HNE.

In silico Antivirus Repurposing and its Modification to Organoselenium Compounds as SARS-CoV-2 Spike Inhibitors.

<b>Background and Objective:</b> The COVID-19, which has been circulating since late 2019, is caused by SARS-CoV-2. Because of its high infectivity, this virus has spread widely throughout the world. Spike glycoprotein is one of the proteins found in SARS-CoV-2. Spike glycoproteins directly affect infection by forming ACE-2 receptors on host cells. Inhibiting glycoprotein spikes could be one method of treating COVID-19. In this study, the antivirus marketed as a database will be repurposed into an antiviral SARS-CoV-2 and the selected compounds will be modified to become organoselenium compounds. <b>Materials and Methods:</b> The research was carried out using <i>in silico</i> methods, such as rigid docking and flexible docking. To obtain information about the interaction between spike glycoprotein and ligands, MOE 2014.09 was used to perform the molecular docking simulation. <b>Results:</b> The analysis of binding energy values was used to select the ten best ligands from the first stage of the molecular docking simulation, which was then modified according to the previous QSAR study to produce 96 new molecules. The second stage of molecular docking simulation was performed with modified molecules. The best-modified ligand was chosen by analyzing the ADME-Tox property, RMSD value and binding energy value. <b>Conclusion:</b> The best three unmodified ligands, Ombitasvir, Elbasvir and Ledipasvir, have a binding energy value of -15.8065, -15.3842 and -15.1255 kcal mol¹, respectively and the best three modified ligands ModL1, ModL2 and ModL3 has a binding value of -15.6716, -13.9489 and -13.2951 kcal mol¹, respectively with an RMSD value of 1.7109 Å, 2.3179 Å and 1.7836 Å.

Modified coptisine derivatives as an inhibitor against pathogenic Rhizomucor miehei, Mycolicibacterium smegmatis (Black Fungus), Monkeypox, and Marburg virus by molecular docking and molecular dynamics simulation-based drug design approach.

During the second phase of SARS-CoV-2, an unknown fungal infection, identified as black fungus, was transmitted to numerous people among the hospitalized COVID-19 patients and increased the death rate. The black fungus is associated with the Mycolicibacterium smegmatis, Mucor lusitanicus, and Rhizomucor miehei microorganisms. At the same time, other pathogenic diseases, such as the Monkeypox virus and Marburg virus, impacted global health. Policymakers are concerned about these pathogens due to their severe pathogenic capabilities and rapid spread. However, no standard therapies are available to manage and treat those conditions. Since the coptisine has significant antimicrobial, antiviral, and antifungal properties; therefore, the current investigation has been designed by modifying coptisine to identify an effective drug molecule against Black fungus, Monkeypox, and Marburg virus. After designing the derivatives of coptisine, they have been optimized to get a stable molecular structure. These ligands were then subjected to molecular docking study against two vital proteins obtained from black fungal pathogens: Rhizomucor miehei (PDB ID: 4WTP) and Mycolicibacterium smegmatis (PDB ID 7D6X), and proteins found in Monkeypox virus (PDB ID: 4QWO) and Marburg virus (PDB ID 4OR8). Following molecular docking, other computational investigations, such as ADMET, QSAR, drug-likeness, quantum calculation and molecular dynamics, were also performed to determine their potentiality as antifungal and antiviral inhibitors. The docking score reported that they have strong affinities against Black fungus, Monkeypox virus, and Marburg virus. Then, the molecular dynamic simulation was conducted to determine their stability and durability in the physiological system with water at 100 ns, which documented that the mentioned drugs were stable over the simulated time. Thus, our in silico investigation provides a preliminary report that coptisine derivatives are safe and potentially effective against Black fungus, Monkeypox virus, and Marburg virus. Hence, coptisine derivatives may be a prospective candidate for developing drugs against Black fungus, Monkeypox and Marburg viruses.

Antiviral Flavonoids: A natural scaffold with prospects as phytomedicines against SARS-CoV2.

Flavonoids are vital candidates to fight against a wide range of pathogenic microbial infections. Due to their therapeutic potential, many flavonoids from the herbs of traditional medicine systems are now being evaluated as lead compounds to develop potential antimicrobial hits. The emergence of SARS-CoV-2 caused one of the deadliest pandemics that has ever been known to mankind. To date, more than 600 million confirmed cases of SARS-CoV2 infection have been reported worldwide. Situations are worse due to the unavailability of therapeutics to combat the viral disease. Thus, there is an urgent need to develop drugs against SARS-CoV2 and its emerging variants. Here, we have carried out a detailed mechanistic analysis of the antiviral efficacy of flavonoids in terms of their potential targets and structural feature required for exerting their antiviral activity. A catalog of various promising flavonoid compounds has been shown to elicit inhibitory effects against SARS-CoV and MERS-CoV proteases. However, they act in the high-micromolar regime. Thus a proper lead-optimization against the various proteases of SARS-CoV2 can lead to high-affinity SARS-CoV2 protease inhibitors. To enable lead optimization, a quantitative structure-activity relationship (QSAR) analysis has been developed for the flavonoids that have shown antiviral activity against viral proteases of SARS-CoV and MERS-CoV. High sequence similarities between coronavirus proteases enable the applicability of the developed QSAR to SARS-CoV2 proteases inhibitor screening. The detailed mechanistic analysis of the antiviral flavonoids and the developed QSAR models is a step forward toward the development of flavonoid-based therapeutics or supplements to fight against COVID-19.

Investigate the designing of eutectic mixture using DFT computation and evaluate their biological potency using molecular docking

Researchers are exploring the eutectic mixture because of their obvious great potential in various disciplines. Herein, authors have presented the DFT calculations, molecular docking and QSAR results for designed eutectic mixtures (EMs) using thiourea and resorcinol on taking different equivalent ratio. Authors have used Jakob et al. method to determine the melting temperature of the systems or EMs theoretically. Thermodynamic parameteres such as the free energy, enthalpy, and other energy of the EMs at room temperature are determined through DFT calculations using Gaussian. Authors have also calculated the physiochemical descriptors of various eutectic mixture based on DFT calculations. Further, molecular docking of the designed EMs is carried out to investigate their biological potential for inhibition of the Mpro of SARS-CoV-2. © 2023 Elsevier B.V.

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

Continuous effort is dedicated to clinically and computationally discovering potential drugs for the novel coronavirus-2. Computer-Aided Drug Design CADD is the backbone of drug discovery, and shifting to computational approaches has become necessary. Quantitative Structure–Activity Relationship QSAR is a widely used approach in predicting the activity of potential molecules and is an early step in drug discovery. 3-chymotrypsin-like-proteinase 3CLpro is a highly conserved enzyme in the coronaviruses characterized by its role in the viral replication cycle. Despite the existence of various vaccines, the development of a new drug for SARS-CoV-2 is a necessity to provide cures to patients. In the pursuit of exploring new potential 3CLpro SARS-CoV-2 inhibitors and contributing to the existing literature, this work opted to build and compare three models of QSAR to correlate between the molecules' structure and their activity: IC50 through the application of Multiple Linear Regression(MLR), Support Vector Regression(SVR), and Particle Swarm Optimization-SVR algorithms (PSO-SVR). The database contains 71 novel derivatives of ML300which have proven nanomolar activity against the 3CLpro enzyme, and the GA algorithm obtained the representative descriptors. The built models were plotted and compared following various internal and external validation criteria, and applicability domains for each model were determined. The results demonstrated that the PSO-SVR model performed best in predictive ability and robustness, followed by SVR and MLR. These results also suggest that the branching degree 6 had a strong negative impact, while the moment of inertia X/Z ratio, the fraction of rotatable bonds, autocorrelation ATSm2, Keirshape2, and weighted path of length 2 positively impacted the activity. These outcomes prove that the PSO-SVR model is robust and concrete and paves the way for its prediction abilities for future screening of more significant inhibitors' datasets. © 2023, The Author(s) under exclusive licence to Associação Brasileira de Engenharia Química.

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

Exploring QSAR models for activity-cliff prediction.

INTRODUCTION AND METHODOLOGY: Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs (ACs). It has been hypothesised that QSAR models struggle to predict ACs and that ACs thus form a major source of prediction error. However, the AC-prediction power of modern QSAR methods and its quantitative relationship to general QSAR-prediction performance is still underexplored. We systematically construct nine distinct QSAR models by combining three molecular representation methods (extended-connectivity fingerprints, physicochemical-descriptor vectors and graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours and multilayer perceptrons); we then use each resulting model to classify pairs of similar compounds as ACs or non-ACs and to predict the activities of individual molecules in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease. RESULTS AND CONCLUSIONS: Our results provide strong support for the hypothesis that indeed QSAR models frequently fail to predict ACs. We observe low AC-sensitivity amongst the evaluated models when the activities of both compounds are unknown, but a substantial increase in AC-sensitivity when the actual activity of one of the compounds is given. Graph isomorphism features are found to be competitive with or superior to classical molecular representations for AC-classification and can thus be employed as baseline AC-prediction models or simple compound-optimisation tools. For general QSAR-prediction, however, extended-connectivity fingerprints still consistently deliver the best performance amongs the tested input representations. A potential future pathway to improve QSAR-modelling performance might be the development of techniques to increase AC-sensitivity.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Virtual Screening of the Flavonoids Compounds with the SARS-CoV-2 3C-like Protease as the Lead Compounds for the COVID-19

As of 1st of September 2020, the COVID-19 pandemic has reached an unprecedented level of more than 25 million cases with more than 850,000 deaths. Moreover, all the drug candidates are still undergoing testing in clinical trials. In this regard, a breakthrough in drug design is neces-sary. One strategy to devise lead compounds is leveraging natural products as a lead source. Sever-al companies and research institutes are currently developing anti-SARS-CoV-2 lead from natural products. Flavonoids are well known as a class of antiviral compounds library. The objective of this research is to employ virtual screening methods for obtaining the best lead compounds from the library of flavonoid compounds. This research employed virtual screening methods that com-prised of downloading the protein and lead compound structures, QSAR analysis prediction, itera-tions of molecular docking simulation, and ADME-TOX simulation for toxicity prediction. The QSAR analysis found that the tested compounds have broad-spectrum antiviral activity, and some of them exhibit specific binding to the 3C-like Protease of the Coronavirus. Moreover, juglanin was found as the compound with the fittest binding with the Protease enzyme of SARS-CoV-2. Al-though most of the tested compounds are deemed toxic by the ADME-Tox test, further research should be conducted to comprehend the most feasible strategy to deliver the drug to the infected lung cells. The juglanin compound is selected as the fittest candidate as the SARS-CoV-2 lead compound in the tested flavonoid samples. However, further research should be conducted to observe the lead delivery method to the cell.Copyright © 2021 Bentham Science Publishers.

Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2.

In viral binding and entry, the Spike(S) protein of SARS-CoV-2 uses transmembrane serine protease 2 (TMPRSS2) for priming to cleavage themselves. In this study, we have screened 'drug-like' 7476 ligands and found that over thirty ligands can effectively inhibit the TMPRSS-2 better than the control ligand. Finally, the three best drug agents L1, L2, and L6 were selected according to their average binding affinities and fitting score. These ligands interact with Asp435, Cys437, Ser436, Trp461, and Cys465 amino acid residues. The three best candidates and a reported drug Nafamostat mesylate (NAM) were selected to run 250 ns molecular dynamics (MD) simulations. Various properties of ligand-protein interactions obtained from MD simulation such as bonds, angle, dihedral, planarity, coulomb, and van der Waals (VdW) were used for principal component analysis (PCA) calculation. PCA discloses the evidence of the structural similarities to the corresponding complexes of L1, L2, and L6 with the complex of TMPRSS2(TM) and Nafamostat mesylate (TM-NAM). Moreover, Quantitative structure-activity relationship (QSAR) pattern recognition was generated using PCA for the investigation of structural similarities among the selected ligands. Multiple Linear Regression (MLR) model was built to predict the binding energy compared to the binding energy obtained from molecular docking. The MLR regression model reveals an accuracy of 80% for the prediction of the binding energy of ligands. ADMET analysis demonstrates that these drug agents are appeared to be safer inhibitors. These three ligands can be used as potential inhibitors against the TMPRSS2.Communicated by Ramaswamy H. Sarma.

Quantitative structure-activity relationships, molecular docking and molecular dynamics simulations reveal drug repurposing candidates as potent SARS-CoV-2 main protease inhibitors.

The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks (q2LOO = 0.60, r2 = 0.80 and r2pred = 0.91), partial-least-square (PLS) regression (q2LOO = 0.83, r2 = 0.62 and r2pred = 0.70) and sequential minimal optimization (SMO) regression (q2LOO = 0.70, r2 = 0.80 and r2pred = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CLpro by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CLpro inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CLpro. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties.Communicated by Ramaswamy H. Sarma.

QSAR, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective benzotriazole-based SARS-CoV 3CL protease inhibitors.

The 3CL Protease of severe acute respiratory syndrome coronavirus (SARS-CoV), responsible for viral replication, has emerged as an essential target for designing anti-coronaviral inhibitors in drug discovery. In recent years, small molecule and peptidomimetic inhibitors have been used to target the inhibition of SARS-CoV 3CL Protease. In this study, we have developed 2D and 3D Quantitative structure activity relationship (QSAR) models on 3CL protease inhibitors with good predictive capability to propose inhibitors with improved affinities. Based on the 3 D contour maps, three new inhibitors were designed in silico, which were further subjected to molecular docking to explore their binding modes. The newly designed compounds showed improved interaction energies toward SARS-CoV-3CLPro due to additional interactions with the active site residues. The molecular docking studies of the most potent compounds revealed specific interactions with Glu 166 and Cys 145. Furthermore, absorption, distribution, metabolism, elimination (ADME) and drug-likeness evaluation revealed improved pharmacokinetic properties for these compounds. The molecular dynamics simulations confirmed the stability of the interactions identified by docking. The results presented would guide the development of new 3CL protease inhibitors with improved affinities in the future.Communicated by Ramaswamy H. Sarma.

Assessment of Structural Basis for Thiazolopyridine Derivatives as DNA Gyrase-B Inhibitors.

BACKGROUND: Tuberculosis (TB) is one of the leading causes of death in the post-COVID- 19 era. It has been observed that there is a devastating condition with a 25-30% increase in TB patients. DNA gyrase B isoform has proved its high potential to be a therapeutically effective target for developing newer and safer anti-TB agents. OBJECTIVE: This study aims to identify minimum structural requirements for the optimization of thiazolopyridine derivatives having DNA gyrase inhibitory activities. Moreover, developed QSAR models could be used to design new thiazolopyridine derivatives and predict their DNA gyrase B inhibitory activity before synthesis. METHODS: 3D-QSAR and Group-based QSAR (G-QSAR) methodologies were adopted to develop accurate, reliable, and predictive QSAR models. Statistical methods such as kNN-MFA SW-FB and MLR SW-FB were used to correlate dependent parameters with descriptors. Both models were thoroughly validated for internal and external predictive abilities. RESULTS: The 3D-QSAR model significantly correlated steric and electrostatic descriptors with q2 0.7491 and predicted r2 0.7792. The G-QSAR model showed that parameters such as SsOHE-index, slogP, ChiV5chain, and T_C_C_3 were crucial for optimizing thiazolopyridine derivatives as DNA gyrase inhibitors. The 3D-QSAR model was interpreted extensively with respect to 3D field points, and the pattern of fragmentation was studied in the G-QSAR model. CONCLUSION: The 3D-QSAR and G-QSAR models were found to be highly predictive. These models could be useful for designing potent DNA gyrase B inhibitors before their synthesis.

Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.

Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3ß, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC50 value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors.

The 3C-like protease (3CLpro), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CLpro of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CLpro in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CLpro. The models were built using a newer objective function optimization of this software, known as the index of ideality correlation (IIC), which provides favorable results. The entire set of molecules was randomly divided into four sets including: active training, passive training, calibration and validation sets. The optimal descriptors were selected from the hybrid model by combining SMILES and hydrogen suppressed graph (HSG) based on the objective function. According to the model interpretation results, eight synthesized compounds were extracted and introduced from the ChEMBL database as good SARS CoV 3CLpro inhibitor. Also, the activity of the introduced molecules further was supported by docking studies using 3CLpro of both SARS-COV-1 and SARS-COV-2. Based on the results of ADMET and OPE study, compounds CHEMBL4458417 and CHEMBL4565907 both containing an indole scaffold with the positive values of drug-likeness and the highest drug-score can be introduced as selected leads.

The design of compounds with desirable properties - The anti-HIV case study.

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.