Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network

Abstract

Although medicinal plants have played an important role in the development of therapeutics as natural compound sources, the synergistic effects of the multiple chemicals in the plants on multiple targets have not been studied widely. Recently, network-based methods for the analysis of drug-target interactions have gained attention and relies on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze the interactions between the chemicals in the medicinal plants and multiple targets based on the complex multipartite network of the medicinal plants, multi-chemicals, and multiple targets. The multipartite network was constructed via the conjunction of two relationships: chemicals in plants and the biological actions of those chemicals on the targets. By analyzing the structural characteristics of the network using the network analysis techniques, we investigated the similarities among the plants via their chemical profiles obtained from the hierarchical clustering analysis. In doing so, we introduced an index of the efficacy of chemicals in a plant on a protein target of interest, called target potency score (TPS). We showed that the analysis can identify specific chemical profiles from each group of plants, which can then be employed for discovering new alternative therapeutic agents. Furthermore, specific clusters of plants and chemicals acting on specific targets were retrieved using TPS that suggested potential drug candidates with high probability of clinical success. We expect that this approach may open a way to predict the biological functions of multi-chemicals and multi-plants on the targets of interest and enable repositioning of the plants and chemicals.