Bioactivity assessment of natural compounds using machine learning models based on drug target similarity

Abstract

Natural products constitute a vast yet largely untapped resource of molecules with therapeutic properties. Computational approaches based on structural similarity offer a scalable approach for evaluating their bioactivity potential. However, this remains challenging due to the immense structural diversity of natural compounds and the complexity of structure-activity relationships. We here assess the bioactivity potential of natural compounds using random forest models utilizing structural fingerprints, maximum common substructure, and molecular descriptors. The models are trained with small-molecule drugs for which the corresponding protein targets are known (1,410 drugs, 0.9 million pairs). Using these models, we evaluated circa 11k natural compounds for functional similarity with therapeutic drugs (1.7 million pairs). The resulting natural compound-drug similarity network consists of several links with support from the published literature as well as links suggestive of unexplored bioactivity of natural compounds. As a proof of concept, we experimentally validated the model-predicted Cox-1 inhibitory activity of 5-methoxysalicylic acid, a compound commonly found in tea, herbs and spices. In contrast, a control compound, with the highest similarity score when using the most weighted fingerprint metric, did not inhibit Cox-1. Our results illustrate the importance of complementing structural similarity with the prior data on molecular interactions, and presents a resource for exploring the therapeutic potential of natural compounds.