Journal of Computer-Aided Molecular Design最新文献

The discovery of dual acetylcholinesterase (AChE) and β-secretase (BACE1) inhibitors remains a promising strategy against multifactorial Alzheimer’s disease. Here, rigorously curated ChEMBL-derived data were used to develop explainable QSAR (Quantitative structure–activity relationship) models for dual-inhibition prioritization. Molecules were standardized, near-duplicates were removed using a Tanimoto similarity threshold (≥ 0.80), and physicochemical outliers were filtered prior to modeling. Multiple classifiers (including Light Gradient-Boosting Machine, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, k-Nearest Neighbors and Gradient Boosting Decision Trees) and fingerprints (e.g., RDKit fingerprints, Extended Connectivity Fingerprint) were benchmarked under scaffold-based nested cross-validation to prevent data leakage. Class imbalance was handled with SMOTETomek applied strictly within training folds. Model selection relied on F-Score, Area Under the Precision–Recall Curve, Matthews Correlation Coefficient (MCC), and Recall, and performance was accompanied by bootstrap confidence intervals, calibration curves, and Y-randomization controls. In classification, the top model (GBDT + ECFP6) achieved strong generalization (Recall ≈ 1.00, PR-AUC ≈ 0.84, MCC ≈ 0.81, F1 Score ≈ 0.84). Shapley Additive Explanations (SHAP) analysis highlighted aromatic and hydrogen-bonding substructures as key positive contributors. Prospective candidates (e.g., CHEMBL5082250, CHEMBL1651126, CHEMBL1651127) were evaluated by active-site-focused docking against AChE (PDB: 4EY7) and BACE1 (PDB: 2G94) with essential waters retained; docking scores (ΔG, kcal·mol⁻¹) were used for relative ranking of the ligands. SwissADME/pkCSM profiling suggested CNS-relevant properties (e.g., MPO, logBB, P-gp liability) and acceptable oral drug-likeness. Collectively, the workflow provides a reproducible and transparent pipeline for prioritizing dual AChE/BACE1 chemotypes and nominates testable scaffolds for experimental validation.

Inhibition of Coagulation Factor X (FXa) is a clinically validated therapeutic strategy; however, developing safer and more selective inhibitors remains a major challenge. In this study, we developed an interpretable machine learning–based QSAR framework to predict both the inhibitory potency and activity class of small molecules targeting FXa. A structurally curated dataset of 6400 compounds was retrieved from ChEMBL, standardized, and encoded using 391 non-redundant Mordred descriptors following systematic filtering. Benchmarking of 42 regression and 42 classification algorithms identified ExtraTreesRegressor and XGBoostClassifier as the most robust models. The regression model achieved an R² of 0.760 and an RMSE of 0.831 on the independent test set, while the classification model reached an accuracy of 0.91 with balanced precision, recall, and an ROC-AUC of 0.962. SHAP (SHapley Additive exPlanations) analysis further enhanced interpretability by revealing that electrostatic, topological, and polar surface descriptors were the dominant contributors to FXa inhibitory potency. Applicability domain assessment using Williams plots confirmed that most compounds in both the training and test sets lay within the model’s reliable prediction space. Overall, the proposed QSAR pipeline integrates strong predictive performance with valuable mechanistic interpretability and rigorous validation, offering a practical computational tool for the virtual screening and rational design of novel FXa inhibitors.

Survivin, a multifunctional regulator of mitosis and apoptosis, plays a central role in cancer progression and therapy resistance, making it an attractive target for anticancer drug development. In this study, a series of 4,6-diphenyl-3-cyanopyridine derivatives were designed and synthesized as potential survivin inhibitors through an integrated strategy combining 3D-QSAR modeling, molecular docking, molecular dynamics simulations, and biological evaluation. The CoMFA and CoMSIA models established reliable structure–activity relationships and provided contour-map-based guidance for rational molecular optimization. Newly designed derivatives displayed enhanced antiproliferative effects against melanoma cells, and computational analyses revealed that the most promising compound showed stable and preferential binding within the BIR domain of survivin, particularly in its dimeric form. These findings demonstrate the effectiveness of contour-guided optimization in discovering novel survivin-targeting scaffolds and highlight 4,6-diphenyl-3-cyanopyridine derivatives as promising leads for further anticancer drug development. Future studies will focus on improving selectivity, clarifying the inhibition mechanism at the molecular level, and evaluating in vivo efficacy.

Flavonoids are found in most edible plants and vegetables and due to their specialized chemical structures and biological activities, we aimed to investigate the efficacy of selected common flavonoids against diabetes-related advance glycation end products (AGEs) through both computational and experimental approaches. Major in silico techniques involved network pharmacology, molecular docking and in vitro AGEs inhibition assays. The pathway enrichment analysis revealed a significant association between AGE regulation and several key biological pathways, including those involved in phenylalanine metabolism, Th17 cell differentiation, and sphingolipid signaling. Molecular docking revealed that hesperidin exhibited the highest binding affinities with transcription regulators 3CJJ (ΔG − 7.1 kJ/mol) and 3TOP (ΔG − 10.0 kJ/mol), while epicatechin showed strong binding to 4F5S (ΔG − 8.3 kJ/mol). All tested compounds significantly reduced oxidative stress, with hesperidin demonstrating moderate inhibition of advanced glycation in the bovine serum albumin (BSA)-glucose model (61.2% ± 1.4%) and BSA-MGO model (52.1% ± 1.7%), as well as potent α-glucosidase inhibition (IC₅₀ = 22.43 ± 1.84 µM). Mechanistic studies further showed moderate protective effects against β-amyloid aggregation and effective trapping of fructosamine and carbonyl groups. The findings suggest that hesperidin and epicatechin possess strong anti-AGEs and anti-inflammatory activities.

Botulinum neurotoxins are class I tier bioterrorism agent, accountable for causing rare but fatal illness ‘botulism’. Out of seven serotypes, A, B, E, and F are responsible for intoxicating humans. Despite knowing harmful effects on human health for centuries, there is no commercial antidote for post-neuronal intoxication is available. In the present study, we report efficacy of regioisomers of [(8-hydroxyquinolin-7-yl)(phenyl) methylamino]benzoic acid against zinc-dependent light chain activities of BoNT/A, /B, /E & /F by combining molecular modeling with in vitro and in vivo studies. Based on structure similarity search, multiple regioisomers of 8-hydroxyquinoline were mined and screened by performing molecular docking. The best-scored compounds were analyzed for inhibitory and binding potential against these serotypes via endopeptidase and surface plasmon resonance assays. The best two compounds (NSC1011 and NSC1012) with potential inhibition and binding kinetics across serotypes were evaluated for therapeutic potential in mouse model. NSC1011 and NSC1012 (regioisomers) docking data revealed their binding energies with active domains of BoNT/A, /B, /E, and /F light chains ranging between − 9.70 to − 4.27, and  − 9.84 to − 7.23 kcal/mol, respectively. The endopeptidase assay displayed ˃ 90% inhibition of catalytic activities, with the IC₅₀ values varying among serotypes from 20 to 40 µM concentrations. SPR interaction of both compounds with the targeted proteins was observed in the range of 3.83E-05 to 4.95E-04 M. These molecules have shown complete protection at one MLD (mouse lethal dose), whereas median extension of animal survival was recorded up to 24 h when exposed to 5X MLD. The in silico, in vitro, and in vivo data reveal that NSC1011 and NSC1012 exhibited good binding affinity, stability, inhibition with promising therapeutic potential against human botulism-causing toxinotypes.