Machine learning-based virtual screening of multi-target anti-obesity compounds from medicinal and edible plants: A combined in silico and in vitro study

Abstract

In response to the limited effectiveness of existing weight loss food products, we sought to apply machine learning-based virtual screening methods to identify potential anti-obesity functional compounds from medicinal and edible plants and validate their in vitro activities. Firstly, we construct and evaluate the machine learning (ML) screening models using Multilayer Perceptron (MLP) and Random Forest (RF) algorithms. The receiver operating characteristic (ROC) curve demonstrates the high accuracy of MLP and RF models in screening for obese-related targets PL (pancreatic lipase) and AMPK (Adenosine 5′-monophosphate activated protein kinase). Subsequently, the tested ML models are employed to screen the constructed database, and Gypenoside LXVI (GYP) and alisol-b-23-acetate (ALI) are identified as compounds exhibiting favorable activity against both targets. The hit compounds are tested for their impact on lipase activity and lipid accumulation. The test results show that GYP and ALI have favorable inhibitory effects on pancreatic lipase (PL), with IC50 of 359.7 and 433.8 μg/mL. Furthermore, both GYP and ALI significantly reduced cellular lipid accumulation by 72.89% and 79.01% with the concentration increase to 40 μg/mL. The molecular docking results indicate that GYP and ALI can interact with several amino acid residues on the two target proteins, thereby affecting the activity of the target proteins. In conclusion, GYP and ALI can prevent and alleviate obesity by inhibiting PL activity and regulating AMPK signaling factors. We innovatively applied virtual screening based on ML to discover functional factors in food for anti-obesity purposes. This novel computational screening technique holds significant potential in the development of dietary supplements to combat obesity.