Target identification among known drugs by deep learning from heterogeneous networks

TLDR

Developing affordable treatments is difficult without complete drug target knowledge. The study develops deepDTnet, a deep learning method for target identification and drug repurposing using a heterogeneous drug‑gene‑disease network. deepDTnet was trained on 732 FDA‑approved small‑molecule drugs and uses a heterogeneous network embedding to predict novel targets. deepDTnet predicted topotecan as a novel ROR‑γt inhibitor (IC50 = 0.43 µM) and showed therapeutic benefit in a mouse multiple sclerosis model, demonstrating its utility for drug repurposing.

Abstract

Without foreknowledge of the complete drug target information, 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 (the area under the receiver operating characteristic curve = 0.963) in identifying novel molecular targets for known drugs, outperforming previously published state-of-the-art methodologies. We then experimentally validate that deepDTnet-predicted topotecan (an approved topoisomerase inhibitor) is a new, direct inhibitor (IC50 = 0.43 μM) of human retinoic-acid-receptor-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 network-based deep learning methodology for target identification to accelerate drug repurposing and minimize the translational gap in drug development.