DeepPurpose: a Deep Learning Based Drug Repurposing Toolkit

With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50 novel architectures. We demonstrated DeepPurpose using case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results. Drug repurposing is about investigating existing drugs for new therapeutic purposes which can potentially speed up drug development 1 . With a large number of existing drugs, it is important to quickly and accurately identify promising candidates for new indications. Especially in facing COVID-19 pandemic today, drug repurposing become particularly relevant as a potentially much faster way to discover effective and safe drugs for treating COVID-19. Deep learning has recently demonstrated its superior performance than classic methods to assist computational drug discovery 2, 3, thanks to its expressive power in extracting, processing and extrapolating patterns in molecular data.