Target Identification Among Known Drugs by Deep Learning from Heterogeneous Networks

Abstract

Without foreknowledge of the complete drug-target network, development of promising and affordable approaches for effective treatment of human diseases is challenging. Here, we develop deepDTnet, a deep learning methodology for new target identification and drug repurposing in a heterogeneous drug-gene-disease network embedding 15 types of chemical, genomic, phenotypic, and cellular network profiles. Trained on 732 U.S. Food and Drug Administration-approved small molecule drugs, deepDTnet shows high accuracy (AUC = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC₅₀=0.43 µM) of human RAR-related orphan receptor-gamma t (ROR-γt). Furthermore, by specifically targeting ROR-γt, topotecan reveals a potential therapeutic effect in a mouse model of multiple sclerosis. In summary, deepDTnet offers a powerful deep learning methodology for network-based target identification to accelerate drug repurposing and minimize the translational gap in drug development.

Keywords: drug-target network, deep learning, heterogeneous networks, topotecan, ROR-γt, multiple sclerosis