Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists

In discovering the potential antagonist of peroxisome proliferator-activated receptor gamma (PPAR gamma), the structure-activity relationship (SAR) is a useful in silico method. However, it is difficult for conventional SAR approaches to predict the activities of antagonists owing to the large structural diversity of antagonistic compounds. This study provides evidence that multi-step SAR screening is applicable for predicting PPAR gamma antagonists by combining different complementary methodologies. We constructed three models: read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. To provide user-customized prediction results, our multi-step SAR screening model combined the three SAR models in a stepwise manner, which subdivided them according to potential levels of the PPAR gamma antagonist. The read-across-like SAR, which considered specific antagonist scaffolds, revealed the highest positive predictive value (PPV). The docking-simulation-interpreting SAR, which considered the molecular surface features, revealed high statistics for the PPV and the true-positive rate (TPR). The deep-learning-based SAR showed the highest TPR at the last classification step. This multi-step SAR screening covered the antagonists of high reliability provided by a read-across-like SAR, as well as the antagonists of diverse scaffolds provided by docking-simulation-interpreting SAR and deep-learning-based SAR. Therefore, to predict PPAR gamma antagonists, multi-step SAR screening could be as a useful tool.

Automated summary

This study utilized a multi-step SAR screening model to predict PPAR gamma antagonists, combining different methodologies including read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. These methods were effectively integrated to provide customized prediction results for users, covering both high reliability and diverse types of antagonists.