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Classification models and SAR analysis on thromboxane A2 synthase inhibitors by machine learning methods.

Y Ji1, R Li1, Y Tian1

  • 1State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China.

SAR and QSAR in Environmental Research
|June 9, 2022
PubMed
Summary
This summary is machine-generated.

This study developed predictive models for Thromboxane A2 synthase (TXS) inhibitors, identifying key structural features like aromatic nitrogenous heterocyclic groups that enhance bioactivity for cardiovascular diseases and cancer drug discovery.

Keywords:
Thromboxane A2 synthase (TXS) inhibitorapplicability domain (AD)deep neural networks (DNN)extreme gradient boosting (XGBoost)structure-activity relationship (SAR)

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Thromboxane A2 synthase (TXS) is a validated drug target for cardiovascular diseases and cancer.
  • Structure-activity relationship (SAR) studies are crucial for optimizing drug candidates.

Purpose of the Study:

  • To conduct an SAR study on 526 TXS inhibitors for bioactivity prediction.
  • To develop and optimize computational models for predicting TXS inhibitor activity.

Main Methods:

  • Utilized MACCS, ECFP4 fingerprints, and MOE descriptors to characterize inhibitors.
  • Developed 24 classification models using SVM, RF, XGBoost, and DNN algorithms.
  • Reduced descriptor sets and constructed simplified models, identifying optimal model parameters.

Main Results:

  • The best performing model (Model_4D) used DNN with 67 MACCS fingerprints, achieving 0.969 prediction accuracy and 0.936 MCC.
  • 91.5% of compounds in the test set fell within the model's application domain with 0.983 accuracy.
  • Identified aromatic nitrogenous heterocyclic groups as beneficial for TXS inhibitor bioactivity.

Conclusions:

  • Developed a highly accurate predictive model for TXS inhibitors.
  • The model's domain of applicability was well-defined and validated.
  • Provided insights into key structural features for designing potent TXS inhibitors.