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Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design.

Pharmaceuticals (Basel, Switzerland)·2026
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Interpretable machine learning-driven QSAR modeling for coagulation factor X inhibitors: from molecular descriptors

Ali Onur Kaya1

  • 1Radiotherapy Department, Vocational School of Health Service, Akdeniz University, 07070, Antalya, Turkey. alionurkaya@akdeniz.edu.tr.

Journal of Computer-Aided Molecular Design
|January 22, 2026
PubMed
Summary

Researchers developed a machine learning model to predict the effectiveness of small molecules targeting Coagulation Factor X (FXa). This interpretable QSAR framework aids in designing safer and more selective FXa inhibitors.

Keywords:
Anticoagulant drug discoveryApplicability domainCoagulation factor XExtraTreesRegressorMachine learningMolecular descriptorsQSAR modelingSHAP analysisXGBoostClassifierpKi prediction

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and medicinal chemistry

Background:

  • Coagulation Factor X (FXa) inhibition is a proven therapeutic approach.
  • Developing safer and more selective FXa inhibitors presents a significant challenge in drug development.

Purpose of the Study:

  • To create an interpretable Quantitative Structure-Activity Relationship (QSAR) framework using machine learning.
  • To predict both inhibitory potency and activity class of small molecules targeting FXa.
  • To facilitate the rational design of novel FXa inhibitors.

Main Methods:

  • A dataset of 6400 compounds targeting FXa was curated from ChEMBL.
  • Compounds were standardized and encoded using 391 Mordred descriptors.
  • Machine learning models (ExtraTreesRegressor, XGBoostClassifier) were benchmarked for regression and classification tasks.
  • SHAP analysis was employed for model interpretability.

Main Results:

  • The ExtraTreesRegressor model achieved R2=0.760 and RMSE=0.831 on an independent test set.
  • The XGBoostClassifier model demonstrated an accuracy of 0.91 and an ROC-AUC of 0.962.
  • SHAP analysis identified electrostatic, topological, and polar surface descriptors as key contributors to FXa inhibition.
  • Applicability domain assessment confirmed the reliability of predictions.

Conclusions:

  • The developed QSAR pipeline offers strong predictive performance and mechanistic interpretability.
  • This computational tool can aid in the virtual screening and design of novel FXa inhibitors.
  • The study provides a validated framework for developing safer and more selective FXa inhibitors.