Comparative Molecular Field Analysis Research Articles

3D-QSAR and molecular docking studies of peptide-hybrids as dengue virus NS2B/NS3 protease inhibitors

Global warming and climate change have made dengue disease a global health issue. More than 50 % of the world's population is at danger of dengue virus (DENV) infection, according to the World Health Organization (WHO). Therefore, a clinically approved dengue fever vaccination and effective treatment are needed. Peptide medication development is new pharmaceutical research. Here we intend to recognize the structural features inhibiting the DENV NS2B/NS3 serine protease for a series of peptide-hybrid inhibitors (R1–R2-Lys-R3-NH2) by the 3D-QSAR technique. Comparative molecular field analysis (q2 = 0.613, r2 = 0.938, r2pred = 0.820) and comparative molecular similarity indices analysis (q2 = 0.640, r2 = 0.928, r2pred = 0.693) were established, revealing minor, electropositive, H-bond acceptor groups at the R1 position, minor, electropositive, H-bond donor groups at the R2 position, and bulky, hydrophobic groups at the R3 position for higher inhibitory activity. Docking studies revealed extensive H-bond and hydrophobic interactions in the binding of tripeptide analogues to the NS2B/NS3 protease. This study provides an insight into the key structural features for the design of peptide-based inhibitors of DENV NS2B/NS3 protease.

Study on β-glucosidase activators by 3D-QSAR, molecular docking and molecular dynamics simulation

To investigate the conformational relationship and mechanism of action of β-glucosidase (BGL) activators, a theoretical study of three-dimensional quantitative structure activity relationship, molecular docking, molecular dynamics (MD) simulations, and binding free energy analysis were performed. The results indicate that comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models had good stability and predictive ability, with r2cv/ r2pred values of 0.540/0.998 and 0.654/0.993, respectively. The derived contour maps of steric, electrostatic, and hydrogen bond donor field further displayed the modified information of these activators. Molecular docking and MD were performed on calcium diascorbate (CaAS) and L-Ascorbyl 2,6-Dipalmitate (L-ADP), which have good activating ability. The study indicates a positive interaction between the activators and the enzyme, and this interaction does not cause irreversible negative effects on the conformation of BGL. The MD results show that the total binding free energies of CaAS and L-ADP with BGL were −19.837 kJ/mol and −37.798 kJ/mol, respectively, and the binding force of the activators with BGL was mainly derived from hydrophobic interaction and hydrogen bonding. This study offers important guidance for the efficient use of BGL and for the research and development of effective activators.

Synthesis, evaluation, and computational chemistry of novel selenenyl sulfides as 3C protease inhibitors with strong cell-based antiviral activity

The 3C protease (3Cpro) is an important enzyme for developing therapeutic medicines against some severe viral infections. In order to discover new classes of antiviral agents, 42 novel selenenyl sulfides were prepared and characterized by 1H NMR, 13C NMR, 77Se NMR and HRMS. The target compounds demonstrated promising inhibitions against 3Cpro from either enterovirus 71 (EV71) or coxsackievirus B3 (CVB3), as well as desirable cell-based antiviral activity against these viruses. Among them, 11f displayed the most potent IC50 value of 0.28 ± 0.05 μM against EV71-3Cpro, and the strongest IC50 value of 0.72 ± 0.15 μM against CVB3, with a selectivity index of 51.5 against Hela cell, superior to the control drug rupintrivir. In addition, molecular docking was undertaken to predict a possible binding mode for compound 11f, and a comparative molecular field analysis (CoMFA) model was subsequently generated to understand the structure-activity relationships. Overall, the present research has provided some new insights to design a distinct family of antiviral compounds.

Strategic development and validation of Isoquinoline-Derived inhibitors targeting ROCK1 kinase

Rho-associated coiled-coil containing kinases (ROCKs) have emerged as pivotal downstream effectors of small guanosine triphosphatases. In recent years, isoquinoline derivatives have garnered substantial attention as potent inhibitors of ROCK1 (RKIs). To delve deeper into their pharmacological properties and to identify potential inhibitors, we conducted an in-depth exploration of a series of RKIs derived from isoquinoline derivatives. This exploration encompassed a multifaceted approach, combining three-dimensional quantitative structure–activity relationship modeling, induced fit docking, and virtual screening. Our comparative molecular field analysis model (q2 = 0.609, R2 = 0.991, ONC = 6, and r2pred = 0.973) and the most robust comparative molecular similarity indices analysis model (q2 = 0.712, R2 = 0.998, ONC = 7, and r2pred = 0.89) exhibited commendable predictive accuracy, bolstered by robust validation parameters. To gain deeper insights into the mechanism of action of isoquinoline-based RKIs, we harnessed precise docking techniques, including induced fit docking, structural interaction fingerprints, and binding pose metadynamics simulations. Employing the PRRR_2 pharmacophore, we meticulously screened Enamine's HLL-460 compound library, unearthing thousands of isoquinoline compounds that aligned with the desired profile. Subsequently, we identified three promising compounds (DS01, DS02, and DS03) through Glide docking, each assessed at varying precision levels. These compounds underwent comprehensive evaluation for their drug-like properties, leveraging QikProp and SwissADME, ultimately unveiling their potential as novel and efficacious RKIs. Furthermore, bolstered by molecular dynamics simulations and validated through activity assays, the bioactivity of the screened molecules DS02, and DS03 was corroborated. DS02 and DS03, previously unreported for their activity against ROCK1, delivered exceptionally promising results. This comprehensive study not only elucidates validated strategies for drug development but also imparts invaluable insights into the design of potent RKIs.

3D-QSAR and Molecular Dynamics Study of Isoxazole Derivatives to Identify the Structural Requirements for Farnesoid X Receptor (FXR) Agonists.

The farnesoid X receptor (FXR) has been recognized as a potential drug target for the treatment of non-alcoholic fatty liver disease (NAFLD). FXR agonists benefit NAFLD by modulating bile acid synthesis and transport, lipid metabolism, inflammation, and fibrosis pathways. However, there are still great challenges involved in developing safe and effective FXR agonists. To investigate the critical factors contributing to their activity on the FXR, 3D-QSAR molecular modeling was applied to a series of isoxazole derivatives, using comparative molecular field analysis (CoMFA (q2 = 0.664, r2 = 0.960, r2pred = 0.872)) and comparative molecular similarity indices analysis (CoMSIA (q2 = 0.706, r2 = 0.969, r2pred = 0.866)) models, which demonstrated strong predictive ability in our study. The contour maps generated from molecular modeling showed that the presence of hydrophobicity at the R2 group and electronegativity group at the R3 group in these compounds is crucial to their agonistic activity. A molecular dynamics (MD) simulation was carried out to further understand the binding modes and interactions between the FXR and its agonists in preclinical or clinical studies. The conformational motions of loops L: H1/H2 and L: H5/H6 in FXR-ligand binding domain (LBD) were crucial to the protein stability and agonistic activity of ligands. Hydrophobic interactions were formed between residues (such as LEU287, MET290, ALA291, HIS294, and VAL297) in helix H3 and ligands. In particular, our study found that residue ARG331 participated in salt bridges, and HIS447 participated in salt bridges and hydrogen bonds with ligands; these interactions were significant to protein-ligand binding. Eight new potent FXR agonists were designed according to our results, and their activities were predicted to be better than that of the first synthetic FXR agonist, GW4064.

Chelation of the Optimal Antifungal Pogostone Analogue with Copper(II) to Explore the Dual Antifungal and Antibacterial Agent.

In an ongoing effort to explore more potent antifungal pogostone (Po) analogues, we maintained the previously identified 3-acetyl-4-hydroxy-2-pyrone core motif while synthesizing a series of Po analogues with variations in the alkyl side chain. The in vitro bioassay results revealed that compound 21 was the most potent antifungal analogue with an EC50 value of 1.1 μg/mL against Sclerotinia sclerotiorum (Lib.) de Bary. Meanwhile, its Cu(II) complex 34 manifested significantly enhanced antibacterial activity against Xanthomonas campestris pv campestris (Xcc) with a minimum inhibitory concentration (MIC) value of 300 μg/mL compared with 21 (MIC = 700 μg/mL). Complex 34 exhibited a striking preventive effect against S. sclerotiorum and Xcc in rape leaves, with control efficacies of 98.8% (50 μg/mL) and 80.7% (1000 μg/mL), respectively. The 3D-QSAR models generated using Topomer comparative molecular field analysis indicated that a shorter alkyl chain (carbon atom number <8), terminal rings, or electron-deficient groups on the alkyl side chain are beneficial for antifungal potency. Further, bioassay results revealed that the component of 21 in complex 34 dominated the antifungal activity, but the introduction of Cu(II) significantly enhanced its antibacterial activity. The toxicological observations demonstrated that 21 could induce abnormal mitochondrial morphology, loss of mitochondrial membrane potential, and reactive oxygen species (ROS) accumulation in S. sclerotiorum. The enzyme assay results showed that 21 is a moderate promiscuous inhibitor of mitochondrial complexes II and III. Besides, the introduction of Cu(II) to 34 could promote the disruption of the cell membrane and intracellular proteins and the ROS level in Xcc compared with 21. In summary, these results highlight the potential of 34 as a dual antifungal and antibacterial biocide for controlling rape diseases or as a promising candidate for further optimization.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches

Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes.

Synthesis, antiproliferative activity, 3D‐QSAR, and molecular docking studies of novel L‐carvone‐derived pyrimidine‐urea compounds

AbstractTo explore novel natural product‐based nitrogen‐containing heterocyclic compounds with antiproliferative activity, 20 L‐carvone‐derived pyrimidine‐urea compounds 4a–4t were synthesized through the multi‐step reaction of L‐carvone, and structurally characterized by Fourier transform infrared (FT‐IR), hydrogen‐1 nuclear magnetic resonance (1H‐NMR), Carbon‐13 nuclear magnetic resonance (13C‐NMR), and High‐resolution mass spectrometry (HRMS). Besides, the in vitro antiproliferative activity of the target compounds against HepG2, Hela, and MCF‐7 cells was evaluated by methyl thiazolyl tetrazolium (MTT) assay. According to the results, the target compounds showed certain inhibitory activities against the tested cancer cell lines, and five compounds (4b, 4h, 4k, 4l, and 4t) exhibited better inhibition activities against Hela cells than the positive control (5‐FU). Among them, compound 4b held significant antiproliferative activities against Hela and HepG2 cells, and thus deserved further study as a leading compound of new anticancer drugs. In addition, an effective and reasonable three‐dimensional quantitative structure‐activity relationships (3D‐QSAR) model was built by the Comparative molecular field analysis (CoMFA) method to analyze the relationship between the structures of the target compounds and their antiproliferative activities (expressed as pIC50) against Hela cells, and proven to have good predictive ability. Molecular docking was carried out to study the possible binding modes of compound 4b and Survivin, and it was found that compound 4b could be well embedded into the active site, along with the formation of several hydrogen bonds and hydrophobic interactions.

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. method: CoMFA, CoMSIA combining HQSAR methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. result: Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA) combining hologram quantitative structure-activity relationship (HQSAR) methods were employed on a series of flavonoids to generate 3D and 2D-QSAR models. Result: The obtained models can be used to predict the activities of new flavonoids and identify the key structural features affecting the NO inhibitory activities. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors. other: none

Discovery of Trifluorobutene Amide Derivatives as Potential Nematicides: Design, Synthesis, Nematicidal Activity Evaluation, SAR, and Mode of Action Study.

Plant-parasitic nematodes are one of the major threats to crop protection. However, only limited nematicides are currently available and are confronted with a growing resistance problem, which necessitates the development of novel nematicides. In this study, a series of trifluorobutene amide derivatives was synthesized through the strategy of amide bond reversal, and their nematicidal activity against Meloidogyne incognita was evaluated. The bioassay showed that compounds C2, C10, and C18 and some analogues thereof exhibited good nematicidal activity. Among them, the derivatives of compound C2 containing a benzene ring [C26 (R = 2-CH3) and C33 (R = 2-Cl)] exhibited excellent bioactivity against M. incognita in vitro. The LC50/72h values reached 14.13 and 14.71 mg·L-1, respectively. Moreover, analogues of compounds C10 and C18 containing a thiophene ring [C43 (R = 5-CH3), C44 (R = 4-CH3), and C50 (R = 5-Cl)] exhibited significant bioactivity against M. incognita in vivo with inhibition rates of 68.8, 65.5, and 69.8% at 2.5 mg·L-1 in a matrix, respectively. Meanwhile, C44 and C50 also showed excellent control effects against M. incognita in both cups and microplots. The structure-activity relationship (SAR) of synthesized compounds was discussed in detail. Comparative molecular field analysis (CoMFA) was also conducted to develop the SAR profile. The preliminary mode of action investigation showed that compound C33 exhibited strong inhibition on egg hatching, motility, feeding behavior, and growth of Caenorhabditis elegans. At the same time, the impact of active compounds on biochemical indicators related to oxidative stress showed that compound C33 influenced the production of ROS (reactive oxygen species), and the accumulation of lipofuscin and lipids on C. elegans.

Essential Structural Profile of Novel Adenosine Derivatives as Antiplatelet Aggregation Inhibitors based on 3D-QSAR Analysis

Aims: In this research, 3D-QSAR evaluation on a set of fresh purinoid compounds that we produced was conducted. This analysis aims to illustrate the correlation between the structure of purine and its ability to prevent platelet aggregation. Our findings could pave the way to discovering novel antithrombotic medications. background: Cardiovascular disease caused by platelet aggregation is a serious threat to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Background: The incidence of cardiovascular disease triggered by the clumping of platelets poses a significant danger to human health. Purine derivatives are important molecules with antiplatelet aggregation activity. Objective: The objectives of this research are to establish the correlation between the structure of purine and its ability to prevent platelet aggregation. Such a correlation could aid in the development of innovative antithrombotic medications. method: In this study, 3D-QSAR analysis was performed on a series of novel purine derivatives synthesized by us based on comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Methods: In this study, 3D-QSAR investigation on a collection of 75 new purine derivatives, which we synthesized, was conducted, utilizing Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). result: Significant correlation coefficients (CoMFA, q2=0.843, r2=0.930, F value=266.755, SEE=0.165; CoMSIA, q2=0.869, r2=0.918, F value=222.571, SEE=0.179) were obtained, and the model prediction ability was validated using the test set. Results: Significant correlation coefficients (CoMFA, q2= 0.843, r2= 0.930, F value= 266.755, SEE= 0.165; CoMSIA, q2= 0.869, r2= 0.918, F value= 222.571, SEE= 0.179) were obtained, and assessed the model's predictive capabilities by validating it with the test set. conclusion: The results suggest that it is beneficial to introduce a group of appropriate size at position C-2 of the purine ring, and that an excessively large group is disadvantageous; a bulky substituent group cannot be directly attached at the C-6 position of the purine ring, and the attachment of a group with low electron cloud density increases the activity, and the attachment of a bulky group at the C-5' of the sugar ring is beneficial, and the presence of hydrogen bond receptors in this region also increases the activity. Conclusion: Our findings indicate that the introduction of an appropriately sized structure at position 2 of the compound yields significant benefits. Conversely, the attachment of an excessively large group is detrimental. Direct attachment of a bulky substituent at C-6 of the compound is not feasible, and its activity increases when the structure with low electron cloud density is added. Moreover, the presence of a voluminous functional group at the 5' position of the compound is advantageous, and its activity will be further increased by the presence of hydrogen bond receptors in this region. These discoveries furnish significant comprehension for the formation of innovative structures with heightened efficacy.

Rational design of novel phenol ether derivatives as non-covalent proteasome inhibitors through 3D-QSAR, molecular docking and ADMET prediction

Aim: The purpose of this paper is to use different structures and ligand-based drug design methods properly to provide theoretical guidance for the design of novel non-covalent proteasome inhibitors, and conduct theoretical analysis of the binding interaction mode between receptors and ligands. At the same time, the pharmacokinetic (PK) prediction, drug-likeness, and synthesis prediction were made for the screened novel drugs. Therefore, potentially attractive non-covalent proteasome inhibitors with low toxicity could be found as anticancer drugs. Methods: In this work, computer-aided drug design methods, including quantitative structure-activity relationship (QSAR), molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) prediction, and drug-likeness prediction methods were performed. Results: In this study, the structure-activity relationship (SAR) of a series of non-covalent proteasome inhibitors were studied and the optimal comparative molecular field analysis (CoMFA; Q2 = 0.574, r2 = 0.999, r2pred = 0.755) and comparative molecular similarity indices analysis (CoMSIA)-SEHA (Q2 = 0.584, r2 = 0.989, r2pred = 0.921) models were obtained. According to the results of the QSAR model, some vital clues were found that would effectively enhance the biological activity of the compound. Based on these clues, 24 novel non-covalent proteasome inhibitors (D01–D24) were finally designed and screened. While the binding models between proteasome [protein data bank (PDB) code: 3MG6] and three representative compounds (15, 20, and D24) were also analyzed by using the molecular docking method. The results suggested that hydrogen bond and hydrophobic interaction played a key role in binding interaction between the receptor and ligand. In addition, the results of ADMET prediction indicated that the new designed compounds had reasonable PK parameters and drug-like properties. Conclusions: These statistical results can provide theoretical guidance for structural optimization, design, and synthesis of more effective non-covalent proteasome inhibitors in the future.

Computation-Directed Molecular Design, Synthesis, and Fungicidal Activity of Succinate Dehydrogenase Inhibitors.

Succinate dehydrogenase inhibitors (SDHIs) are a class of fungicides targeting the pathogenic fungi mitochondrial SDH. Here, molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics (MD) simulations were used to guide SDHI innovation. Molecular docking was performed to explore the binding modes of SDH and its inhibitors. 3D-QSAR models were carried out on 33 compounds with activity against Rhizoctonia cerealis (R. cerealis); their structure-activity relationships were analyzed using comparative molecular field analysis and comparative molecular similarity indices analysis. MD simulations were used to assess the stability of the complexes under physiological conditions, and the results were consistent with molecular docking. Binding free energy was calculated through the molecular mechanics generalized born surface area method, and the binding free energy was decomposed. The results are consistent with the activity of bioassay and indicate that van der Waals and lipophilic interactions contribute the most in the molecular binding process. Afterward, we designed and synthesized 12 compounds under the guidance of the above-mentioned analyses, bioassay found that F9 was active against R. cerealis with the EC50 value of 9.43 μg/mL, and F4, F5, and F9 were active against Botrytis cinerea with an EC50 values of 5.80, 3.17, and 1.63 μg/mL, respectively. They all showed good activity between positive controls of pydiflumetofen and thifluzamide. Our study provides new considerations for effective SDHIs discovery.

Mechanistic insights into the selectivity of bicyclophosphorothionate antagonists for housefly versus rat GABA receptors.

γ-Aminobutyric acid (GABA) receptors (GABARs) are validated targets of insecticides. Bicyclophosphorothionates are a group of insecticidal compounds that act as noncompetitive antagonists of GABARs. We previously reported that the analogs exhibit various degrees of selectivity for housefly versus rat GABARs, depending on substitutions at the 3- and 4-positions. We here sought to elucidate the unsolved mechanisms of the receptor selectivity using quantitative structure-activity relationship (QSAR), molecular docking, and molecular dynamics approaches. Three-dimensional (3D)-QSAR studies using Topomer comparative molecular field analysis quantitatively demonstrated how the introduction of a small alkyl group at the 3-position of bicyclophosphorothionates contributes to the housefly versus rat GABAR selectivity. To investigate the molecular mechanisms of the selective action, bicyclophosphorothionates were docked into housefly Resistance to dieldrin (RDL) GABAR and rat α1β2γ2 GABAR homology models built using the published 3D-structures of human GABARs as templates. The results of molecular docking and molecular dynamics simulations revealed that the 2'Ala and 6'Thr residues of the RDL subunit within the channel are the key amino acids for binding to the housefly GABARs, whereas the 2'Ser residue of γ2 subunit plays a crucial role in binding to rat GABARs. We revealed the molecular mechanisms underlying the selective antagonistic action of bicyclophosphorothionates on housefly versus rat GABARs. The information presented should help design and develop novel, safe GABAR-targeting insecticides. © 2023 Society of Chemical Industry.

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

Modelling of novel bornoel analogs as Influenza A Virus inhibitors through genetic function approximation, comparative molecular fields, molecular docking, and ADMET/Pharmacokinetic studies

Influenza A Virus (IAV) is a human respiratory pathogen prone to mutations and genome re-assortment leading to global pandemics. In this study, we applied the molecular modelling strategies such as, two-dimensional (2D), three-dimensional (3D)-quantitative structure–activity relationship (QSAR), and molecular docking simulation on a novel series of borneol compounds as influenza inhibitors. The best developed 2D-QSAR models, MLR (Q2 ​= ​0.8735, R2(train) ​= ​0.9096) and ANN [3-2-1] (Q2 ​= ​0.8987, R2(train) ​= ​0.9171) revealed good and acceptable statistical validation metrics for the inhibitory activity predictions. The 3D-QSAR models were generated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), which showed CoMFA_S ​+ ​E (Q2 ​= ​0.559, R2(train) ​= ​0.939) and CoMSIA_S ​+ ​E (Q2 ​= ​0.577, R2(train) ​= ​0.941) as the best-observed models in accordance with the model acceptability standards. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrates the steric and electrostatic molecular field relationships with the inhibitory effects of the studied molecules. Moreover, the binding modes of the active ligands were studied through molecular docking simulation with the Human Hemagglutinin (HA) receptor of influenza A virus (A/Puerto Rico/8/34(H1N1)). The studied compounds revealed the formation of H-bonds, CH-bonds, and hydrophobic interactions with the active amino acid residues such as Asn 543, Asn 614, Asn 617, Leu 618, Ser 540, Lys 539, and Lys 621 in the HA binding cavity. The prediction of drug-likeness and ADMET properties of the compounds revealed their good bioavailability and pharmacokinetic profiling. This study may provide a valuable in-silico guideline for discovering novel potent influenza inhibitors.

Design of novel small molecules derived from styrylpyridine as potent HDAC1 inhibitors for the treatment of gastric cancer using 3D-QSAR, drug similarity, ADMET prediction, molecular docking, and molecular dynamics studies

Histone deacetylase (HDAC) dysregulation plays an important role in cancer progression and is an important therapeutic target for anticancer drug development. A series of molecules derived from styrylpyridine have HDAC inhibitory activity for the treatment of gastric cancer and could have great potential as drugs against gastric cancer.The objective of this work is to modify the styrylpyridine backbone in order to design new inhibitors with high activity and favorable pharmacokinetic properties for drug discovery. Based on the three-dimensional quantitative structure- activity study, robust and reliable comparative molecular field analysis and comparative molecular similarity index analysis models were developed and validated, then used to design seven new molecules and predict in silico their biological activity.As a result, the seven newly designed molecules have a higher biological activity than the synthesized template molecule N21. The designed molecules are submitted to tests for drug-like properties, pharmacokinetics properties, and molecular docking. These tests enabled us to select the two newly-designed molecules T5 and T6 as the best HDAC1 inhibitors, compared with the model molecule N21. Subsequently, molecular dynamics simulations were carried out to study the stability of styrylpyridine derivatives in the active site of HDAC1.The designed molecules T5 and T6 have the potential to be drugs for treating gastric cancer. The synthesis, in vitro and in vivo evaluation of the biological activity of newly designed molecules T5 and T6 are interesting proposal for the conception of new drugs to treat gastric cancer.

Structural optimization of pyrrolopyrimidine BTK inhibitors based on molecular simulation.

BTK is a critical regulator involved in the proliferation, differentiation, and apoptosis of B cells. BTK inhibitors can effectively alleviate various diseases such as tumors, leukemia, and asthma. During this study, a range of novel BTK inhibitors were designed using 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. We selected 41 pyrrolopyrimidine derivatives as BTK inhibitors to structure a 3D-QSAR model. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were adopted to research the connection between the pharmacological activities and chemical structures of the compounds. The CoMFA model (q2= 0.519,R2= 0.971), CoMSIA model (q2= 0.512,R2= 0.990), and external validation demonstrated excellent predictive performance and reliability of the 3D-QSAR model. We designed eight novel molecules with higher inhibitory activities according to the three-dimensional equipotential fields and explored the interactions between the compounds and BTK by molecular docking, which showed that the novel molecules had higher binding affinities with BTK than the template molecule 18. Then, the results of molecular docking were further verified by MD simulation, which showed that amino acid residues such as Leu528, Val416, and Met477 played vital parts in the interaction, and the binding free energy analysis showed that the novel molecules had higher stability with BTK. Finally, the ADME/T properties were predicted for all of the novel compounds, and the results showed that the majority of them had favorable pharmacokinetic properties. Therefore, this study provides strong support for the development of novel BTK inhibitors.

3D-QSAR Combination with Molecular Dynamics Simulations to Effectively Design the Active Ryanodine Receptor Agonists against Spodoptera frugiperda.

Computer-aided molecular modeling was applied to design a series of Spodoptera frugiperda RyR agonists. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. MD simulations in the complex with S. frugiperda native, mutant RyR, and mammalian RyR1 under physiological conditions were used to validate the detailed binding mechanism. Binding free energy calculation by molecular mechanics generalized surface area (MM-GBSA) explained the role of key amino acid residues in ligand-receptor binding. Therefore, 14 new compounds were effectively designed and synthesized, and a bioassay indicated that compounds A-2 and A-3 showed comparable activity to that of chloranthraniliprole with LC50 values of 0.27, 0.18, and 0.20 mg L-1, respectively, against S. frugiperda. Most target compounds also displayed good activity against Mythinma separata at 0.1 mg L-1. Molecular docking and MM-GBSA calculations demonstrated that A-3 had a better binding capacity with native and mutant S. frugiperda RyRs.