Integrated QSAR, Docking, and Molecular Dynamics‐Based Discovery of 1,4‐Naphthoquinone Derivatives as Potential Anti‐Tuberculosis Agents

A Comprehensive Study Utilizing QSAR, Virtual Screening, Molecular Docking, Molecular Dynamics, and MM/GBSA Analyses Reveals Natural Diterpenoids as Promising Caspase-1 Inhibitors

This study highlights natural diterpenoids and coumarin glycosides as promising scaffolds for caspase-1 inhibition and demonstrates that integrating QSAR modeling with structure-based approaches provides an efficient strategy for discovering potential anti-inflammatory drug candidates.

In Silico Optimization of Dihydropteridine Derivatives Targeting PLK1 for Glioblastoma: An Integrated QSAR, Docking, and Molecular Dynamics Study

Computational findings support the prioritization of X14 for further experimental validation in glioblastoma therapy, and generally favorable ADMET profiles were observed, hepatotoxicity alerts were predicted for all compounds, which represents an important limitation supporting the prioritization of X14.

QSAR Modeling of 1,3,4-Oxadiazole Derivatives for Predicting Anticancer Activity Using SO-PLS Analysis.

INTRODUCTION The study aimed to develop a QSAR model for a series of 1,3,4- oxadiazole derivatives to identify molecular determinants influencing anticancer activity and provide a predictive framework for rational drug design. METHODS A QSAR model was constructed using the Simulated Orthogonal Projections to Latent Structures (SO-PLS) approach. Five molecular field descriptors, steric (gauss_s), electrostatic (gauss_e), hydrophobic (gauss_h), hydrogen-bond acceptor (gauss_a), and hydrogen-bond donor (gauss_d), were employed. The model was developed on a training set of 36 compounds and evaluated using Partial Least Squares regression with up to five latent factors. Y-randomization was applied to assess model robustness. RESULTS The model demonstrated an excellent fit (R² = 0.931) and acceptable predictive ability (R²-CV = 0.478), with Y-randomization confirming robustness (R²-scrambled = 0.679). Field contribution analysis revealed steric (29.52%) and hydrophobic (24.92%) effects as the primary determinants of biological activity, followed by hydrogen-bond donor (18.81%) and acceptor (18.02%) contributions, while electrostatic interactions (8.73%) were less influential. Regression coefficient analysis identified molecular regions where targeted substitutions could enhance activity. DISCUSSION The findings highlight the critical role of steric and hydrophobic interactions in modulating the anticancer activity of oxadiazole derivatives. Strategic molecular modifications guided by field contributions and regression analysis can improve potency, supporting rational design of novel oxadiazole-based anticancer agents. CONCLUSION This QSAR model provides a robust predictive tool for designing 1,3, 4-oxadiazole derivatives with enhanced anticancer activity.

Design of novel derivatives of 1,3,4-thiadiazole against the α-amylase enzyme using 3D-QSAR, ADMET evaluation, docking analysis, molecular dynamic simulations and MM-PBSA approaches

Findings suggest that PR1 and PR2 are promising candidates for advanced antidiabetic drug development, exhibiting predicted enhanced inhibitory activities and favorable pharmacokinetic and toxicological profiles.

Atom-based 3D-QSAR, pharmacophore modelling and molecular dynamics guided identification of thienopyrimidinone derivatives targeting EGFR in breast cancer.

Breast cancer still ranks to be one of the primary causes of cancer-related death worldwide, underscoring the critical need for innovative targeted treatment agents with enhanced safety and efficacy. The current work examined Thienopyrimidinone-based derivatives as possible EGFR inhibitors for breast cancer treatment using an integrated computational drug discovery approach. An atom-based 3D-QSAR model was developed and statistically validated, exhibiting satisfactory predictive performance (R2 = 0.88 and Q2 = 0.55) and showed strong predictive numbers when checked contour maps. Hydrophobic and electron-withdrawing substitutions around the core scaffold were predicted to contribute significantly to biological activity. Pharmacophore modelling identified key structural features associated with EGFR inhibitory activity, including hydrophobic interactions, aromatic ring contacts, and hydrogen bond donor/acceptor sites, which facilitated ligand recognition. ADMET prediction and Lipinski's Rule of Five evaluation indicated that several designed derivatives possessed favourable predicted pharmacokinetic properties, favourable predicted pharmacokinetic properties and oral bioavailability, and satisfactory drug-likeness. Molecular docking studies against the mutant EGFR kinase domain (PDB ID: 6LUD) revealed favourable binding of the selected lead compounds within the ATP-binding site, supported by hydrogen-bonding and hydrophobic interactions. These investigations also revealed considerable hydrogen bonding, hydrophobic interactions, and target bound conformational orientation similar to that of reference compounds. A 100-ns molecular dynamics simulation of the EGFR-Mol 13 complex further demonstrated overall complex stability, with persistent protein-ligand interactions and adaptive ligand conformational behaviour. Overall, the integrated QSAR, pharmacophore modelling, molecular docking, ADMET prediction, and molecular dynamics analyses suggest that thienopyrimidinone derivatives represent promising computational lead compounds for the future design and experimental evaluation of EGFR-targeted therapies for breast cancer.

Integrated QSAR, Electrochemical, and Computational Evaluation of Green Synthesized Pyrimidine-Based Inhibitors for Copper Corrosion in 3.5% NaCl