ArticleJournal of computer-aided molecular design2026
Interpretable machine learning-driven QSAR modeling for coagulation factor X inhibitors: from molecular descriptors to predictive potency.
Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Development of an Interpretable QSAR Model for Predicting Coagulation Factor XIIa Inhibitors Using Ensemble Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design.Pharmaceuticals (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 R
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