Kampo drug repositioning: prediction of new indications of Kampo medicines by big data and machine learning

Abstract

Kampo medicines (Japanese traditional formulas) are popular and useful for treatment of multifactorial and chronic diseases. However, the pharmacotherapy with Kampo medicines depends heavily on the empirical knowledge of medical doctors in practice, and scientific evidence is not sufficient for explaining the underlying mode-of-action of Kampo medicines. Pharmacological effects of Kampo medicines are based on multiple compound?multiple target interactions. In this study we propose new computational methods for predicting new therapeutic indications of Kampo medicines from various big data of Kampo medicines and crude drugs. Target proteins and target pathways of the constituent compounds of Kampo medicines were estimated by docking simulations and machine learning methods based on large-scale omics data (e.g., genome, proteome, metabolome, chemical-protein interactome), and potential therapeutic indications of Kampo medicines were predicted on a large scale. We also established KampoDB (http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a novel database of Kampo medicines, which provides various useful scientific resources on Kampo medicines, crude drugs, constituent compounds, and target proteins of the constituent compounds.