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Updated: Jan 24, 2026

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
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Modelado QSAR interpretable basado en aprendizaje automático para inhibidores del factor X de coagulación: de
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
Resumen
Los investigadores desarrollaron un modelo de aprendizaje automático para predecir la eficacia de moléculas pequeñas dirigidas al Factor Xa (FXa). Este marco QSAR interpretable ayuda en el diseño de inhibidores de FXa más seguros y selectivos.
Conclusiones:
- La canalización QSAR desarrollada ofrece un fuerte rendimiento predictivo e interpretabilidad mecanicista.
- Esta herramienta computacional puede ayudar en el cribado virtual y el diseño de nuevos inhibidores de FXa.
- El estudio proporciona un marco validado para el desarrollo de inhibidores de FXa más seguros y selectivos.
Palabras clave:
descubrimiento de fármacos anticoagulantesdominio de aplicabilidadFactor X de coagulaciónExtraTreesRegressoraprendizaje automáticodescriptores molecularesmodelado QSARanálisis SHAPXGBoostClassifierpredicción de pKiMás Videos Relacionados
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