3D-QSAR, docking and ADMET properties of aurone analogues as antimalarial agents

In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations

Based on a structural family of thirty-two NR2B-selective N-Methyl-D-Aspartate receptor (NMDAR) antagonists, two phenylpiperazine derivatives labeled C37 and C39 were conceived thanks to molecular modeling techniques, as novel NMDAR inhibitors exhibiting the highest analgesic activities (of pIC50 order) against neuropathic pain, with excellent ADME-toxicity profiles, and good levels of molecular stability towards the targeted protein of NMDA receptor. Initially, the quantitative structure-activity relationships (QSARs) models were developed using multiple linear regression (MLR), partial least square regression (PLSR), multiple non-linear regression (MNLR), and artificial neural network (ANN) techniques, revealing that analgesic activity was strongly correlated with dipole moment, octanol/water partition coefficient, Oxygen mass percentage, electronegativity, and energy of the lowest unoccupied molecular orbital, whose the correlation coefficients of generated models were: 0.860, 0.758, 0.885 and 0.977, respectively. The predictive capacity of each model was evaluated by an external validation with correlation coefficients of 0.703, 0.851, 0.778, and 0.981 respectively, followed by a cross-validation technique with the leave-one-out procedure (CVLOO) with Q2cv of 0.785, more than Y-randomization test, and applicability domain (AD), in addition to Fisher's and Student's statistical tests. Thereafter, ten novel molecules were designed based on MLR QSAR model, then predicted with their ADME-Toxicity profiles and subsequently examined for their similarity to the drug candidates. Finally, two of the most active compounds (C37 and C39) were chosen for molecular docking and molecular dynamics (MD) investigations during 100 ns of MD simulation time in complex with the targeted protein of NMDA receptor (5EWJ.pdb).

Scaffold-Hopping Strategies in Aurone Optimization: A Comprehensive Review of Synthetic Procedures and Biological Activities of Nitrogen and Sulfur Analogues

Aurones, particular polyphenolic compounds belonging to the class of minor flavonoids and overlooked for a long time, have gained significative attention in medicinal chemistry in recent years. Indeed, considering their unique and outstanding biological properties, they stand out as an intriguing reservoir of new potential lead compounds in the drug discovery context. Nevertheless, several physicochemical, pharmacokinetic, and pharmacodynamic (P3) issues hinder their progression in more advanced phases of the drug discovery pipeline, making lead optimization campaigns necessary. In this context, scaffold hopping has proven to be a valuable approach in the optimization of natural products. This review provides a comprehensive and updated picture of the scaffold-hopping approaches directed at the optimization of natural and synthetic aurones. In the literature analysis, a particular focus is given to nitrogen and sulfur analogues. For each class presented, general synthetic procedures are summarized, highlighting the key advantages and potential issues. Furthermore, the biological activities of the most representative scaffold-hopped compounds are presented, emphasizing the improvements achieved and the potential for further optimization compared to the aurone class.

Leveraging computational tools to combat malaria: assessment and development of new therapeutics

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

Discovery of potential epidermal growth factor receptor inhibitors from black pepper for the treatment of lung cancer: an in-silico approach

A tyrosine kinase receptor known as epidermal growth factor receptor (EGFR) is one of the main tumour markers in many cancer types and also plays a crucial role in cell proliferation, differentiation, angiogenesis, and apoptosis, which is a result of the auto-phosphorylations (kinase activity enhancement) that trigger signals involved in different cellular processes. Due to the discovery that non-small cell lung cancer (NSCLC) is a cause of this kinase activity enhancement, so far, several inhibitors have been tested against EGFR, but the side effects of these inhibitors necessitate an urgent measure to come up with an inhibitor that will be more specific to the cancer cells and not affect self-cells. This study was conducted to evaluate the efficacy of 37 compounds derived from Piper nigrum against EGFR using computer-aided drug design. Based on molecular docking, induced-fit docking, calculation of free binding energy, pharmacokinetics, QSAR prediction, and MD simulation. We propose five (5) lead compounds (clarkinol A, isodihydrofutoquinol B, Burchellin, kadsurin B, and lancifolin C) as a novel inhibitor, with clarkinol A demonstrating the highest binding affinity (-7.304 kcal/mol) with EGFR when compared with the standard drug (erlotinib). They also showed significant moderation for parameters investigated for a good pharmacokinetic profile, with a reliable R2 coefficient value predicted using QSAR models. The MD simulation of clarkinol A was found to be stable within the EGFR binding pocket throughout the 75 ns simulation run time. The findings showed that clarkinol A derived from Piper nigrum is worth further investigation and consideration as a possible EGFR inhibitor for the treatment of lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00197-1.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity. The aim of this study is to modify the structure of these molecules to obtain new molecules with improved TRK inhibitory activity and pharmacokinetic properties favorable to the design of new drugs. Based on these series, we carried out a 3D-QSAR study. As a result, robust and reliable CoMFA and CoMSIA models are developed and applied to the design of 11 new molecules. These new molecules have a biological activity superior to the most active molecule in the starting series. The eleven designed molecules are screened using drug-likeness, ADMET proprieties, molecular docking, and MM-GBSA filters. The results of this screening identified the T1, T3, and T4 molecules as the best candidates for strong inhibition of TRKs enzymatic activity. In addition, molecular dynamics simulations are performed for TRK free and complexed with ligands T1, T3, and T4 to evaluate the stability of ligand-protein complexes over the simulation time. On the other hand, we proposed experimental synthesis routes for these newly designed molecules. Finally, the designed molecules T1, T2, and T3 have great potential to become reliable candidates for the conception of new drug inhibitors of TRKs.Communicated by Ramaswamy H. Sarma.

Investigation of Bucillamine as anti-COVID-19 drug: DFT study, molecular docking, molecular dynamic simulation and ADMET analysis

The novel coronavirus disease-2019 (COVID-19), caused by SARS-CoV-2, is a global health pandemic beginning in early December 2019 in Wuhan, Hubei province, China. The effective drug target among coronaviruses is the SARS-CoV-2 main protease (Mpro), because of its crucial role in processing viral polyproteins translated from the viral RNA. In this study, the bioactivity of the selected thiol drug named Bucillamine (BUC) was evaluated as a potential drug for COVID-19 treatment by using computational modeling strategies. First, the molecular electrostatic potential density (ESP) calculation was performed to estimate the chemically active atoms of BUC. Additionally, BUC was docked to the Mpro (PDB: 6LU7) to evaluate the protein-ligand binding affinities. Besides, the estimated ESP results by density functional theory (DFT) were used to illustrate the molecular docking findings. Moreover, the frontier orbitals analysis was calculated to determine the charge transfer between the Mpro and BUC. Then, the stability of protein-ligand complex was subjected to the molecular dynamic simulations. Finally, an in silico study was performed to predict drug-likeness and absorption, distribution, metabolism, excretion and toxicity profiles (ADMET) of BUC. These results propose that BUC can be a potential drug candidate against the COVID-19 disease progression.Communicated by Ramaswamy H. Sarma.

Molecular mechanism of Acanthopanax senticosus in the treatment of Alzheimer's disease based on network pharmacology and molecular docking

Alzheimer's disease is the most common neurodegenerative disease. Acanthopanax senticosus, also known as Ciwujia or Siberian ginseng in Chinese, has a wide range of antioxidant and anti-inflammatory activities. The study aims to explore the action mechanism of A. senticosus against Alzheimer's disease using network pharmacology and molecular docking. The active ingredients and targets of A. senticosus were searched through the ETCM database, and Alzheimer's disease-related targets were obtained through the OMIM and GeneCards databases. The Cytoscape 3.7.2 software was used to construct a "drug-component-target" relationship network, and the target genes of A. senticosus against Alzheimer's disease were imported into the String database to establish a protein interaction (PPI) network. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene enrichment analyses were performed through the Metascape database to obtain potential pathways of action of A. senticosus for the treatment of Alzheimer's disease, and the ability of these active ingredients to bind to core targets was then verified by molecular docking. 51 active ingredients were screened from A. senticosus, and 88 effective targets for Alzheimer's disease were screened. Topological and pathway-enrichment analyses revealed that A. senticosus could play a beneficial role in the treatment of Alzheimer's disease by regulating apoptosis and inflammation. Molecular docking results showed that Ciwujianoside B, Chiisanoside, and Ciwujianoside D1 had strong binding abilities to key target proteins (TNFα, IL1β, and CASP3). Collectively, A. senticosus is feasible in the treatment of Alzheimer's disease.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

Discovery of anti-colon cancer agents targeting wild-type and mutant p53 using computer-aided drug design

Mutations in the p53 gene are common and occur in over 50% of all cancers, as it is involved in DNA damage repair, cell cycle regulation and apoptosis. Moreover, the p53 gene is mutated in 70% of colon cancers. Therefore, the development of drugs to combat this mutation requires urgent attention. With this in mind, in silico drug design approaches were applied on quinoline derivatives with anticancer activity. In 3D-QSAR study, steric, electrostatic, hydrophobic and H-bond acceptor fields (SEHA) play an important role in prediction and design of new colon cancer compounds. Indeed, the two best CoMSIA/SEHA models with (Q2 = 0.737, R2 = 0.914, R pred 2 = 0.720) and (Q2 = 0.738, R2 = 0.919, R pred 2 = 0.739) show good prediction of human colon carcinoma HCT 116 (p53+/+) and (p53-/-) activities, respectively. Furthermore, the predictive ability and robustness of these models were tested by several validation methods. Molecular docking analyses reveal crucial interactions with the active sites of the p53 protein in both wild type and mutant. Based on these theoretical studies, we designed 10 new compounds with good anticancer activity potential, which were evaluated using ADMET properties. Molecular dynamics simulations were performed to confirm the detailed binding mode of the docking results. Finally, the MM-GBSA based on molecular dynamics simulation confirmed that the designed compounds were able to form stable hydrogen bonding interactions with the crucial residues, which are essential to overcome the p53 mutation in colon cancer.Communicated by Ramaswamy H. Sarma.

A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored.Communicated by Ramaswamy H. Sarma.

In silico identification of 1,2,4-triazoles as potential Candida Albicans inhibitors using 3D-QSAR, molecular docking, molecular dynamics simulations, and ADMET profiling

Fluconazole and Voriconazole are individual antifungal inhibitors broadly adopted for treating fungal infections, including Candida Albicans. Unfortunately, these medicines clinically used have significant side effects. Consequently, the improvement of safer and better therapy became more indispensable. In this study, a set of 27 1,2,4-triazole compounds have been tested as potential Candida Albicans inhibitors by using different theoretical methods. The created comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) contour maps significantly impacted the development of novel Candida Albicans inhibitors with valuable activities. The mode of interactions between the 1,2,4-triazole inhibitors and the targeted receptor was studied by molecular docking simulation. The proposed new molecule P1 showed satisfied stability in the active pocket of the targeted receptor compared to the more active molecule in the dataset compared to Fluconazole medication. Meanwhile, the binding energy obtained by molecular docking for molecule P1 is - 9.3 kcal/mol compared with - 6.7 kcal/mol for Fluconazole medication. Also, MM/GBSA value obtained by molecular dynamics simulations at 100 ns for molecule P1 is - 33.34 kcal/mol compared with - 15.85 kcal/mol for Fluconazole medication. In addition, molecule P1 showed good oral bioavailability and was non-toxic according to ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Therefore, the results indicated compound P1 might be a future inhibitor of Candida Albicans infection.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers

Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor for D-2HG formation in mutant IDH enzymes is a challenging task in cancer research. The mutation in the cytosolic IDH1 enzyme at R132H, especially, may be associated with higher frequency of all types of cancers. So, the present work specifically focuses on the design and screening of allosteric site binders to the cytosolic mutant IDH1 enzyme. The 62 reported drug molecules were screened along with biological activity to identify the small molecular inhibitors using computer-aided drug design strategies. The designed molecules proposed in this work show better binding affinity, biological activity, bioavailability, and potency toward the inhibition of D-2HG formation compare to the reported drugs in the in silico approach.

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q² = 0.63, R² = 0.83 and Rpred2 = 0.70) and the CoMSIA (Q² = 0.584, R² = 0.816, and Rpred2= 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis.Communicated by Ramaswamy H. Sarma.

Structure- and ligand-based drug design methods for the modeling of antimalarial agents: a review of updates from 2012 onwards

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.

A new perspective to improve the treatment of Lianhuaqingwen on COVID-19 and prevent the environmental health risk of medication

Lianhuaqingwen (LH), one traditional Chinese medicine (TCM), has been used to treat the coronavirus disease 2019 (COVID-19), but its ecotoxicity with potential human health security has not been well investigated. To overcome such adverse effects and improve its medication efficacy, an intelligent multi-method integrated dietary scheme, screening, and performance evaluation approach was developed. Thirteen LH compounds were selected, and the main protease (Mpro) was used as the potential drug target. Resulted information showed that the more compounds of LH added, the higher medication efficacy obtained using multi-method integrated screening system, expert consultation method, and molecular dynamics simulation. Pharmacodynamic mechanism analysis showed that low total energy and polar surface area of LH active compound (i.e., β-sitosterol) will contribute to the best therapeutic effect on COVID-19 using quantitative structure-activity relationships (QSAR) and sensitivity models. Additionally, when mild COVID-19 patients take LH with the optimum dietary scheme (i.e., β-lactoglobulin, α-lactalbumin, vitamin A, vitamin B, vitamin C, carotene, and vitamin E), the medication efficacy were significantly improved (23.58%). Pharmacokinetics and toxicokinetics results showed that LH had certain human health risks and ecotoxicity. This study revealed the multi-compound interaction mechanism of LH treatment on COVID-19, and provided theoretical guidance for improving therapeutic effect, evaluating TCM safety, and preventing human health risk.

Evaluation of flavonoids as potential inhibitors of the SARS-CoV-2 main protease and spike RBD: Molecular docking, ADMET evaluation and molecular dynamics simulations

The 3CLpro main protease and the RDB spike (s) protein of SARS-CoV-2 are critical targets in the treatment of coronavirus 19 disease (COVID-19), as they are responsible for the COVID-19 replication and infection. With this in mind, Molecular docking of 26 natural compounds belonging to the flavonoid family with the 3CLpro and RBD sites of SARS-CoV-2 has been performed. The docking results revealed that the ligands Silibinin, Tomentin A, Tomentin B, 4′-O-methyldiplacone, Hesperidin Amentoflavone and Bilobetin act as a potential inhibitor of SARS-CoV-2 3CLpro, and that the ligands Herbacetin, Morin, Silibinin, Tomentin E, Amentoflavone, Bilobetin, Baicalein and Quercetin can be potential inhibitors of SARS-CoV-2 RBD. It has been noticed that three ligands can inhibit both sites of SARS-CoV-2, indicating a great potential of these compounds to combat COVID-19. Moreover, molecular docking has been validated by a new validation method based on visual inspiration. Evaluation of ADMET pharmacokinetic properties and the drug likeness in silico revealed that six compounds could be effective drugs against COVID-19. Finally, the docking results were verified by molecular dynamics simulations and MM-GBSA calculation to confirm the stability of hydrogen bonding interactions with crucial residues, which are essential to overcome SARS-CoV-2. These results could direct researchers toward plant-derived compounds that could be further investigated as therapeutic targets against COVID-19 replication and infection.

2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents

Objectives

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC.

Methods

The two dimensional-quantitative structure-activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively.

Results

The model generated via 2D-QSAR modeling was highly predictive with R²= 0.798, R²adj = 0.754, Q²CV = 0.673, R² test = 0.531, and cRp² = 0.627 assessment parameters. Molecular docking-based virtual screening identified compounds 25, 32, 15, 21, and 23 with the highest MolDock scores as the best hits, of which compound 25 had the highest MolDock score of -138.329 kcal/mol. All of the identified hits had higher MolDock scores than the standard drug (osimertinib). The best hit compounds were ascertained to be drug-like in nature following the Lipinski's rule of five. Also, their ADMET features displayed average pharmacokinetic profiles.

Conclusion

After successful preclinical testing, the hit compounds identified in this study may serve as potential NSCLC therapeutic agents due to their safety and efficacy with the exception of compound 23, which was found to be toxic. They can also serve as a template for designing novel NSCLC therapeutic agents.

In silico analysis of noscapine compounds as anti-tumor agents targeting the tubulin receptor

Objective

This research aims to develop a mathematical model that relates the structural features of noscapine with anti-tumor activity, to explains the mode of binding between noscapine compounds and the target receptor tubulin by docking analysis. By considering the results of docking analysis and predictions of pharmacokinetic properties/drug likeness, we designed novel noscapine compounds as anti-tumor agents against pancreatic cancer.

Methods

We used an in silico quantitative structure–activity relationship (QSAR) approach, molecular docking analysis and online tools for pharmacokinetics and drug likeness prediction to develop novel compounds.

Results

A QSAR model with good validations parameters and quality of fit (R² = 0.9731, Q²_CV = 0.9434, R²_adj = 0.9647 and R²_{test set} = 0.8343) was built utilizing 70% of the dataset as a training set and the remaining 30% as an external validation to ascertain its predictive capability. Three novel compounds were designed: D3, D4 and D6 with binding scores of −11.2, −10.2 and 10.6 kcal/mol, respectively, exhibiting high affinity towards the tubulin receptor than the template (parent compound) and the co-crystallized ligand (E∗) with a binding score of 9.2 kcal/mol.

Conclusion

The QSAR approach and molecular docking analysis is an important approach for modern drug discovery. Pharmacokinetics studies of the selected novel compounds revealed good drug properties and can be used as candidate compounds for the development of anti-tumor agents for pancreatic cancer.

3D-QSAR, ADME-Tox In Silico Prediction and Molecular Docking Studies for Modeling the Analgesic Activity against Neuropathic Pain of Novel NR2B-Selective NMDA Receptor Antagonists

A new class of selective antagonists of the N-Methyl-D-Aspartate (NMDA) receptor subunit 2B have been developed using molecular modeling techniques. The three-dimensional quantitative structure–activity relationship (3D-QSAR) study, based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models, indicate that steric, electrostatic and hydrogen bond acceptor fields have a key function in the analgesic activity against neuropathic pain. The predictive accuracy of the developed CoMFA model (Q2 = 0.540, R2 = 0.980, R2 pred = 0.613) and the best CoMSIA model (Q2 = 0.665, R2 = 0.916, R2 pred = 0.701) has been successfully examined through external and internal validation. Based on ADMET in silico properties, L1, L2 and L3 ligands are non-toxic inhibitors of 1A2, 2C19 and 2C9 cytochromes, predicted to passively cross the blood–brain barrier (BBB) and have the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicate that the active ligands (L1, L2 and L3) interact specifically with Phe176, Glu235, Glu236, Gln110, Asp136 and Glu178 amino acids of the transport protein encoded as 3QEL. Therefore, they could be used as analgesic drugs for the treatment of neuropathic pain.

QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia

Forty-four bicyclo ((aryl) methyl) benzamides, acting as glycine transporter type 1 (GlyT1) inhibitors, are developed using molecular modeling techniques. QSAR models generated by multiple linear and non-linear regressions affirm that the biological inhibitory activity against the schizophrenia disease is strongly and significantly correlated with physicochemical, geometrical and topological descriptors, in particular: Hydrogen bond donor, polarizability, surface tension, stretch and torsion energies and topological diameter. According to in silico ADMET properties, the most active ligands (L6, L9, L30, L31 and L37) are the molecules having the highest probability of penetrating the central nervous system (CNS), but the molecule 32 has the highest probability of being absorbed by the gastrointestinal tract. Molecular docking results indicate that Tyr124, Phe43, Phe325, Asp46, Phe319 and Val120 amino acids are the active sites of the dopamine transporter (DAT) membrane protein, in which the most active ligands can inhibit the glycine transporter type 1 (GlyT1). The results of molecular dynamics (MD) simulation revealed that all five inhibitors remained stable in the active sites of the DAT protein during 100 ns, demonstrating their promising role as candidate drugs for the treatment of schizophrenia.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Synthetic Derivatives against Wild-Type and Non-Wild-Type Sporothrix brasiliensis: In Vitro and In Silico Analyses

Recently, the well-known geographically wide distribution of sporotrichosis in Brazil, combined with the difficulties of effective domestic feline treatment, has emphasized the pressing need for new therapeutic alternatives. This work considers a range of synthetic derivatives as potential antifungals against Sporothrix brasiliensis isolated from cats from the hyperendemic Brazilian region. Six S. brasiliensis isolates from the sporotrichotic lesions of itraconazole responsive or non-responsive domestic cats were studied. The minimum inhibitory concentrations (MICs) of three novel hydrazone derivatives and eleven novel quinone derivatives were determined using the broth microdilution method (M38-A2). In silico tests were also used to predict the pharmacological profile and toxicity parameters of these synthetic derivatives. MICs and MFCs ranged from 1 to >128 µg/mL. The ADMET computational analysis failed to detect toxicity while a good pharmacological predictive profile, with parameters similar to itraconazole, was obtained. Three hydrazone derivatives were particularly promising candidates as antifungal agents against itraconazole-resistant S. brasiliensis from the Brazilian hyperendemic region. Since sporotrichosis is a neglected zoonosis currently spreading in Latin America, particularly in Brazil, the present data can contribute to its future control by alternative antifungal drug design against S. brasiliensis, the most virulent and prevalent species of the hyperendemic context.

3D-QSAR Studies of 1,2,4-Oxadiazole Derivatives as Sortase A Inhibitors

Sortase A (SrtA) is an enzyme that catalyzes the attachment of proteins to the cell wall of Gram-positive bacterial membrane, preventing the spread of pathogenic bacterial strains. Here, one class of oxadiazole compounds was distinguished as an efficient inhibitor of SrtA via the "S. aureus Sortase A" substrate-based virtual screening. The current study on 3D-QSAR was done by utilizing preparation of the structure in the Schrödinger software suite and an assessment of 120 derivatives with the crystal structure of 1,2,4-oxadiazole which was extracted from the PDB data bank. The docking operation of the best compound in terms of pMIC (pMIC = 2.77) was done to determine the drug likeliness and binding form of 1,2,4-oxadiazole derivatives as antibiotics in the active site. Using the kNN-MFA way, seven models of 3D-QSAR were created and amongst them, and one model was selected as the best. The chosen model based on q 2 (pred_r 2) and R 2 values related to the sixth factor of PLS illustrates better and more acceptable external and internal predictions. Values of crossvalidation (pred_r 2), validation (q 2), and F were observed 0.5479, 0.6319, and 179.0, respectively, for a test group including 24 molecules and the training group including 96 molecules. The external reliability outcomes showed that the acceptable and the selective 3D-QSAR model had a high predictive potential (R 2 = 0.9235) which was confirmed by the Y-randomization test. Besides, the model applicability domain was described successfully to validate the estimation of the model.

Computer-aided design of some quinazoline analogues as epidermal growth factor receptor inhibitors

Background

The treatment of epidermal growth factor receptor (EGFR)-muted non-small cell lung cancer (NSCLC) remains among the utmost important unachieved therapeutic need worldwide. Development of EGFR inhibitors to treat NSCLC mutations has been among the difficult tasks faced by researchers in this area. As such, there is a need to discover more EGFR inhibitors. The purpose of this work is to perform computer-aided/structure-based design of novel EGFR inhibitors, elucidate their nature of interactions with their target, and also assess their ADMET properties as well as their drug-likeness, respectively. Compound 17 with a highest binding affinity of −9.5kcal/mol was identified as the template hit compound using molecular docking virtual screening in our previous work. The compound interacted with the active site of the EGFR receptor via hydrogen bond with the following amino acid residues MET793, MET793, THR854, and ASP855 with bond distances of 2.61394 (Å), 2.18464 (Å), 2.57601 (Å), and 2.68794 (Å), respectively. It also interacted with the active site of the EGFR receptor via halogen bond (GLN791), hydrophobic bond (LEU718, CYS797, LYS745, ALA743, ALA743, and VAL726), electrostatic bond (LYS745), and others (MET766), respectively. Furthermore, from our previous study, the following descriptors (ATSC6m, ATSC8e, MATS7m, SpMax3_Bhp, SpMax5_Bhs, and MaxHBint10) contained in the reported model were found to be responsible for the inhibitory activities of the studied compounds. In this research, the template (compound 17) was modified manually by attaching halo-phenyl and halo-phenyl-amino rings on the para position of the flouro-nitro-benzamide moiety of the template compound, respectively.

Results

A computer-aided design/structure-based approach was used to design six new EGFR inhibitors using molecule 17 as the template compound for the design identified in our previously reported work. Molecular docking investigation was performed to elucidate the binding mode of these newly designed EGFR inhibitors with the binding pose of EGFR receptor (pdb code 4ZAU) and found to have better affinities which range from −9.5 to −10.4 kcal/mol than the template compound and gefitinib, the control, respectively. The ADMET property assessment of these newly designed EGFR inhibitors indicated that they were orally bioavailable with good absorption, distribution, metabolism, and excretory properties with no toxicity. And for their drug-likeness, they were seen to have a higher molecular weight which might be as a result of halo-phenyl-amino ring attachments. Based on this finding, halo-phenyl-amino rings might be responsible for the inhibitory activities of these newly designed compounds.

Conclusion

The six newly designed EGFR inhibitors were found to have higher binding affinities toward their target EGFR receptor than the template compound and gefitinib which was used as the control in this research. They were seen to have good ADMET and drug-like properties which indicate that they might be orally bioavailable. Furthermore, according to their synthetic accessibility score, they can be easily synthesized in the laboratory because the values were found to be less than five which fall within the easy portion of the scale. Therefore, this research recommends that these newly designed EGFR inhibitors should be synthesized most especially those with higher binding affinities, good ADMET, and drug-likeness properties than the template compound.

Design of more potent quinazoline derivatives as EGFRWT inhibitors for the treatment of NSCLC: a computational approach

Background

Lung cancer remains the leading and deadly type of cancer worldwide. It was estimated to account for about 25% of the 7 million people that died as a result of cancer-related issues/mortality every year in the world. Non-small cell lung cancer (NSCLC) is the lethal/deadly class of lung cancer with nearly 1.5 million reported cases and less than 20% survival rate. Therefore, it becomes necessary to explore more effective NSCLC drugs.

Result

A computational approach was employed here to design ten new EGFRWT inhibitors using compound 18 as a template for the design identified with the best binding affinity and good pharmacokinetic properties previously reported in our work. The modeled inhibitory activities of these newly designed EGFRWT inhibitors (range from 7.746966 to 11.09261) were better than that of the hit compound with pIC50 of 7.5639 and gefitinib the positive control with pIC50 of 5.879426. The ligand-binding interaction between these newly designed EGFRWT inhibitors and the EGFR tyrosine kinase receptor as shown in Table 3 was investigated and elucidated using molecular docking protocol. Based on the molecular docking results, the binding affinities of these newly designed EGFRWT inhibitors were found to be between − 8.8 and − 9.5 kcal/mol. The designed compound SFD10 has the highest binding affinity of − 9.5 kcal/mol followed by compound SFD8 (with a binding affinity of − 9.3 kcal/mol), then by compound SFD9 and 4 (each with a binding affinity of − 9.3 kcal/mol). None of them was found to have more than one violation of the filtering criterion used in this study thereby showing good ADMET properties.

Conclusion

The modeled inhibitory activities and binding affinities of these newly designed EGFRWT inhibitors were found to be higher than that of the template compound and the control (gefitinib) used in this research. They were also seen to be non-toxic with good pharmacokinetic properties.

Potential Benefits of Flavonoids on the Progression of Atherosclerosis by Their Effect on Vascular Smooth Muscle Excitability

Flavonoids are a group of secondary metabolites derived from plant-based foods, and they offer many health benefits in different stages of several diseases. This review will focus on their effects on ion channels expressed in vascular smooth muscle during atherosclerosis. Since ion channels can be regulated by redox potential, it is expected that during the onset of oxidative stress-related diseases, ion channels present changes in their conductive activity, impacting the progression of the disease. A typical oxidative stress-related condition is atherosclerosis, which involves the dysfunction of vascular smooth muscle. We aim to present the state of the art on how redox potential affects vascular smooth muscle ion channel function and summarize if the benefits observed in this disease by using flavonoids involve restoring the ion channel activity.

Prediction of potential inhibitors of SARS-CoV-2 using 3D-QSAR, molecular docking modeling and ADMET properties

Coronavirus (COVID-19), an enveloped RNA virus, primarily affects human beings. It has been deemed by the World Health Organization (WHO) as a pandemic. For this reason, COVID-19 has become one of the most lethal viruses which the modern world has ever witnessed although some established pharmaceutical companies allege that they have come up with a remedy for COVID-19. To that end, a set of carboxamides sulfonamide derivatives has been under study using 3D-QSAR approach. CoMFA and CoMSIA are one of the most cardinal techniques used in molecular modeling to mold a worthwhile 3D-QSAR model. The expected predictability has been achieved using the CoMFA model (Q2 = 0.579; R2 = 0.989; R2test = 0.791) and the CoMSIA model (Q2 = 0.542; R2 = 0.975; R2test = 0.964). In a similar vein, the contour maps extracted from both CoMFA and CoMSIA models provide much useful information to determine the structural requirements impacting the activity; subsequently, these contour maps pave the way for proposing 8 compounds with important predicted activities. The molecular surflex-docking simulation has been adopted to scrutinize the interactions existing between potentially and used antimalarial molecule on a large scale, called Chloroquine (CQ) and the proposed carboxamides sulfonamide analogs with COVID-19 main protease (PDB: 6LU7). The outcomes of the molecular docking point out that the new molecule P1 has high stability in the active site of COVID-19 and an efficient binding affinity (total scoring) in relation with the Chloroquine. Last of all, the newly designed carboxamides sulfonamide molecules have been evaluated for their oral bioavailability and toxicity, the results point out that these scaffolds have cardinal ADMET properties and can be granted as reliable inhibitors against COVID-19.

A computational approach for designing novel SARS-CoV-2 Mpro inhibitors: combined QSAR, molecular docking, and molecular dynamics simulation techniques

The promising compound T₂₁ for treating COVID-19 at the active site of SARS-CoV-2 M^pro.