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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Related Experiment Video

Updated: Jun 27, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design.

Ali Onur Kaya1, Mert Can Emre2, Nesrin Emre3

  • 1Radiotherapy Department, Health Services Vocational School, Akdeniz University, 07070 Antalya, Türkiye.

Pharmaceuticals (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

A machine learning framework accurately predicts coagulation factor XIa (FXIa) inhibitors, aiding safer anticoagulant drug discovery. This interpretable quantitative structure-activity relationship (QSAR) model supports virtual screening and medicinal chemistry optimization for FXIa targets.

Keywords:
FXIa inhibitorsSHAP analysisapplicability domaindrug designmachine learning (ML)quantitative structure–activity relationship (QSAR)

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Coagulation factor XIa (FXIa) is a key target for developing safer anticoagulants with reduced bleeding risk.
  • Existing anticoagulant therapies carry a significant risk of bleeding complications.
  • Novel therapeutic strategies targeting FXIa are needed to improve patient safety.

Purpose of the Study:

  • To develop an interpretable machine learning-driven quantitative structure-activity relationship (QSAR) framework.
  • To predict the inhibitory activity of FXIa inhibitors.
  • To support virtual screening applications for FXIa inhibitor discovery.

Main Methods:

  • Utilized 3026 compounds from the ChEMBL database for regression and 2119 for classification modeling.
  • Generated molecular descriptors using RDKit, Mordred, and Morgan fingerprints.
  • Employed and benchmarked various machine learning algorithms, including nonlinear ensemble methods, with rigorous validation techniques.

Main Results:

  • Optimized HistGradientBoostingRegressor achieved R² of 0.711 and RMSE of 0.759 for regression.
  • Classification models demonstrated accuracies approaching 95%.
  • SHAP analysis revealed key predictive features including lipophilicity, aromatic structure, and electrostatic properties; virtual screening identified promising candidate compounds.

Conclusions:

  • The developed ML-driven QSAR framework is robust, interpretable, and reproducible for FXIa inhibitor discovery.
  • This strategy can facilitate virtual screening and medicinal chemistry optimization for FXIa-targeted anticoagulants.
  • The findings contribute to the advancement of safer anticoagulant drug discovery.