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Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump.

Kevin S McLoughlin1, Claire G Jeong2, Thomas D Sweitzer2

  • 1Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94501, United States.

Journal of Chemical Information and Modeling
|January 27, 2021
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Summary
This summary is machine-generated.

Developing accurate in silico models to predict bile salt export pump (BSEP) inhibition from chemical structures can reduce drug discovery costs and prevent liver injury. Our new models show improved performance for predicting BSEP inhibition.

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Area of Science:

  • Drug-induced liver injury
  • Pharmacology
  • Computational toxicology

Background:

  • Drug inhibition of bile salt transporters, like the bile salt export pump (BSEP), is a common cause of cholestatic liver injury.
  • Predictive in silico models for BSEP inhibition are crucial for cost-effective drug discovery and patient safety.

Purpose of the Study:

  • To develop and validate novel classification and regression models for predicting BSEP inhibition directly from chemical structures.
  • To improve upon the performance of existing in silico models for BSEP inhibition.

Main Methods:

  • Systematic evaluation of various chemical featurization techniques.
  • Assessment of different data set partitioning strategies.
  • Analysis of various class labeling methods for model training and validation.

Main Results:

  • Developed classification and regression models demonstrating substantially improved predictive performance for BSEP inhibition compared to prior models.
  • Identified optimal featurization, partitioning, and labeling methods that enhance model generalization to new chemical entities.
  • Validated model performance on novel chemical structures.

Conclusions:

  • The developed in silico models offer a reliable approach for predicting BSEP inhibition.
  • Optimized computational methods can significantly enhance the accuracy and generalizability of predictive toxicology models.
  • These findings can aid in reducing drug discovery costs and mitigating the risk of drug-induced liver injury.