TL;DR
An interpretable computational framework is developed for modeling COX-1 inhibitor activity and characterizing prioritized compounds at the molecular level by integrating QSAR modeling, explainable artificial intelligence, molecular docking, molecular dynamics, and MM/GBSA calculations to investigate COX-1 inhibitors.
These findings identified CMNPD11687 as a promising marine-derived scaffold for PIM-1 inhibition and demonstrated the value of combining ML-driven screening with molecular simulation to accelerate early-stage anticancer drug discovery.
Machine learning accelerates GPCR ligand discovery but often lacks interpretability and struggles to generalise to structurally novel chemical space. We present an integrated framework combining ensemble learning, SHAP interpretability, and complementary structure-based analyses to prioritise putative candidates for th...
An explainable, assay-aware, and leakage-safe machine learning framework for predicting thrombin-inhibitory activity provides useful predictions within the represented chemical space and measurable generalization for unseen scaffolds.
Beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1) plays a central role in amyloid-β production and remains an important therapeutic target for Alzheimer’s disease. This study developed an integrated quantitative structure–activity relationship and molecular dynamics (QSAR–MD) framework to identify promisin...
Background/Objective: Activated coagulation factor XII (FXIIa) is a component of the contact activation pathway and a pharmacologically relevant target in contact-system-associated processes. In this study, scaffold-aware and interpretable machine-learning QSAR models were developed for human FXIIa activity. Methods: B...
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