QSAR of Novel Chrysin Benzimidazole Derivatives for Potential Anti-Cancer Agents

Rational Design and In Silico Evaluation of Benzimidazole Derivatives as Potential Antimalarial Agents

Quantitative Structure–Activity Relationship (QSAR) and molecular docking studies were performed on a series of 40 benzimidazole analogues to identify structural features associated with antimalarial activity. CoMFA, CoMSIA, and HQSAR models developed using a training set of 32 compounds demonstrated good statistical reliability and predictive ability. Molecular docking against the Plasmodium falciparum* target protein (PDB ID: 2ANL) revealed favorable binding interactions, supporting the design of more potent antimalarial candidates. Based on the computational results, 15 novel benzimidazole derivatives were designed, synthesized, characterized, and evaluated for their antimalarial activity. The experimental results further validated the developed QSAR models, confirming their usefulness in the rational design of new antimalarial agents. Keywords: Benzimidazole; Antimalarial drug discovery; QSAR modeling; CoMFA; CoMSIA; Molecular docking; Plasmodium falciparum; Structure-based drug design

2D-QSAR Modeling of Quinazoline Derivatives as EGFR Inhibitors for Colorectal Cancer

Background: Globally, colorectal cancer represents one of the most common types of cancer, along with being one of the top 10 causes of cancer-related deaths. Epidermal Growth Factor Receptors (EGFRs) are a major target for colorectal cancer, and quinazoline derivatives have shown anticancer activity as EGFR inhibitors. Purpose: The objective of this research was the development of a two-dimensional quantitative structure–activity relationship model (2D-QSAR); that is, to develop a simple way to identify the structural components that help predict a potent compound with the highest predicted activity among the series of quinazoline derivatives for EGFR inhibitors for colorectal cancer. Methods: A dataset of 21 quinazoline derivatives was collected from the literature, molecular descriptors were calculated, and Partial Least Squares (PLS) regression was used to develop statistically significant QSAR models. Generated and validated using internal and external validation parameters. Results: Model-1 (RANDOM_70_30_SFB_PLS_TRIALS_2) showed the best statistical performance with r² = 0.6538, q² = 0.5314, pred-r² = 0.6940, and an F-test value of 22.6638. The descriptors SsssNcount and SaaCHE-index were identified as significant contributors to EGFR inhibitory activity, indicating that tertiary nitrogen count and charge distribution play crucial roles in determining biological potency. Conclusion: The developed 2D-QSAR model identified a potent compound with the highest predicted biological activity as an EGFR inhibitor for colorectal cancer.

QSAR, molecular dynamics, and biological evaluation of novel myeloperoxidase inhibitors via ligand-based pharmacophore modeling as potential anticancer agents

The findings from molecular docking, 500-ns molecular dynamics simulations, MM-GBSA calculations, and alanine scanning analyses collectively corroborate a stable binding mode of BTB11556 within the MPO active site, support further investigation of BTB11556 as a candidate compound associated with MPO-targeted therapeutic strategies.

A Comprehensive Computational Investigation of Novel Hydrazide–Hydrazone Derivatives as Potential EGFR Inhibitors for Gastric Cancer Treatment

Findings support the Hydrazide–hydrazone derivatives' potential as EGFR kinase inhibitors, with M3 emerging as a particularly promising lead compound.

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.

Synthesis of Novel Benzoxazole–Piperazine Hybrids as Anticancer Leads: Biological, Molecular Docking, and ADMET Evaluations