Article

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach

Authors: Shuo Gu1, Lu-hua Lai1
1 BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, and the Center for Quantitative Biology, Peking University, Beijing 100871, China
Correspondence to: Lu-hua Lai: lhlai@pku.edu.cn,
DOI: 10.1038/s41401-019-0306-9
Received: 13 June 2019
Accepted: 29 August 2019
Advance online: 17 September 2019

Abstract

Chinese herbal medicine (CHM) addresses complex diseases through polypharmacological interactions. However, systematic studies of herbal medicine pharmacology remain challenging due to the complexity of CHM ingredients and their interactions with various targets. In this study, we aim to address this challenge with computational approaches. We investigated the herb-target-disease associations of 197 commonly prescribed CHMs using the similarity ensemble approach and DisGeNET database. We demonstrated that this method can be applied to associate herbs with their putative targets. In the case study of three well-known herbs, Radix Glycyrrhizae, Flos Lonicerae, and Rhizoma Coptidis, approximately 70% of the predicted targets were supported by scientific literature. By linking 406 targets to 2439 annotated diseases, we further analyzed the pharmacological functions of 197 herbs. Finally, we proposed a strategy of target-oriented herbal formula design and illustrated the target profiles for four common chronic diseases, namely, Alzheimer’s disease, depressive disorder, hypertensive disease, and non-insulin-dependent diabetes mellitus. This computational approach holds great potential in the target identification of herbs, understanding the molecular mechanisms of CHM, and designing novel herbal formulas.
Keywords: Chinese herbal medicine; similarity ensemble approach; target prediction; herb-target-disease association; target-oriented herbal formula design

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