Machine Learning Assisted Discovery of Novel p38α Inhibitors from Natural Products

Background: P38α, emerging as a hot spot for drug discovery, is a member of the mitogen- activated protein kinase (MAPK) family and plays a crucial role in regulating the production of inflammatory mediators. However, despite a massive number of highly potent molecules being reported and several under clinical trials, no p38α inhibitor has been approved yet. There is still demand to discover novel p38α to deal with the safety issue induced by off-target effects. Objective: In this study, we performed a machine learning-based virtual screening to identify p38α inhibitors from a natural products library, expecting to find novel drug lead scaffolds. Methods: Firstly, the training dataset was processed with similarity screening to fit the chemical space of the natural products library. Then, six classifiers were constructed by combing two sets of molecular features with three different machine learning algorithms. After model evaluation, the three best classifiers were used for virtual screening. Results: Among the 15 compounds selected for experimental validation, picrasidine S was identified as a p38α inhibitor with the IC50 as 34.14 μM. Molecular docking was performed to predict the interaction mode of picrasidine S and p38α, indicating a specific hydrogen bond with Met109. Conclusion: This work provides a protocol and example for machine learning-assisted discovery of p38α inhibitor from natural products, as well as a novel lead scaffold represented by picrasidine S for further optimization and investigation.