Author

Ali Onur Kaya

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Open access Sep 2026

Development of an Interpretable QSAR Model for Predicting Coagulation Factor XIIa Inhibitors Using Ensemble Machine Learning

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...

Ali Onur Kaya, Mert Can Emre · Pharmaceuticals · 0 citations

Open access Sep 2026

An Explainable Machine Learning-Based QSAR Framework for Predicting Thrombin Inhibitory Activity

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.

Ali Onur Kaya, Mert Can Emre · Pharmaceuticals · 0 citations