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Updated: Jul 4, 2026

Protein Target Prediction and Validation of Small Molecule Compound

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Machine learning methods and docking for predicting human pregnane X receptor activation.

Akash Khandelwal¹, Matthew D Krasowski, Erica J Reschly

¹Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, USA.

Chemical Research in Toxicology

|June 13, 2008

Summary

Computational models accurately predict pregnane X receptor (PXR) activators and nonactivators. These methods enhance virtual screening for potential drug-drug interactions before in vitro testing.

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Last Updated: Jul 4, 2026

Protein Target Prediction and Validation of Small Molecule Compound

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Area of Science:

Pharmacology and Cheminformatics
Computational Toxicology

Background:

The pregnane X receptor (PXR) is crucial for managing xenobiotic metabolism and transport.
In vitro assays are standard for screening PXR agonists, but computational approaches offer scalable alternatives.

Purpose of the Study:

To develop and validate computational models for predicting human PXR activators and nonactivators.
To assess the utility of VolSurf descriptors and machine learning algorithms (RP, RF, SVM) for PXR activity prediction.

Main Methods:

Utilized recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms.
Employed VolSurf descriptors for feature generation.
Validated models using 10-fold randomization and independent test sets (n=15 and n=145).
Compared performance with FlexX docking and logistic regression.

Main Results:

Models achieved high prediction accuracy for activators (82.6-98.9%) and good accuracy for nonactivators (62.0-88.6%) in randomized validation.
Test set validation showed improved accuracy (80-93.3% for n=15) compared to previous models.
Predictions remained within the model's applicability domain, confirmed by principal component analysis.
RP, RF, and SVM models outperformed FlexX docking for classification.

Conclusions:

VolSurf descriptors combined with machine learning (RP, RF, SVM) provide accurate and reliable predictions for PXR activation.
These computational methods are suitable for high-throughput virtual screening to identify potential PXR modulators.
This approach can help predict drug-drug interactions early in the drug discovery process, reducing the need for extensive in vitro testing.