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.
Background and purpose: PIM-1 kinase, a serine/threonine kinase implicated in several cancers, has emerged as a promising yet underexplored target for anticancer therapy. This study aimed to identify the potential of PIM-1 inhibitors from marine natural products by integrating machine learning (ML)-based quantitative s...
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.
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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