Affiliation:
1. Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education; Yunnan Provincial Center
for Research & Development of Natural Products; School of Chemical Science and Technology, Yunnan University,
Kunming, 650091, China
Abstract
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.
Method:
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.
Publisher
Bentham Science Publishers Ltd.
Subject
Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine