Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct...

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• The pregnane X receptor (PXR) plays a crucial role in metabolizing xenobiotics and endobiotic substances.
• PXR activation can reduce the efficacy of small-molecule drugs and lead to drug-drug interactions.
• Predicting PXR activators using machine learning (ML) is challenging due to PXR's ligand promiscuity and flexible binding pocket.

Purpose of the Study:

• To develop and validate a novel regularization technique for machine learning models to predict PXR activators.
• To improve the prediction of PXR activity for compounds structurally dissimilar to those in training datasets.
• To identify novel PXR activators through validated computational methods.

Main Methods:

• Implementation and evaluation of random forest and support vector machine models.
• Development of a novel regularization technique penalizing the gap between training and validation performance.
• Experimental validation of computationally selected compounds using cellular PXR ligand-binding domain assembly assays.

Main Results:

• Classical ML training procedures showed limitations in predicting PXR activity for dissimilar compounds.
• The novel regularization technique improved Matthew correlation coefficients (MCCs) by up to 0.21 on a challenging test set.
• Twelve out of 31 structurally distinct compounds selected by regularized ML models were confirmed as PXR activators.

Conclusions:

• The developed regularization technique enhances the predictive power of ML models for PXR activators.
• This approach successfully identified novel PXR-activating compounds with potential therapeutic implications.
• The findings contribute to more accurate in silico drug screening and drug-drug interaction prediction.