Before revision:  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 .
After revision:  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 DL architectures. To tackle method development challenges, DeepPurpose also supports various data split settings and preloads five benchmarking datasets. We demonstrated that DeepPurpose allows users to obtain state-of-the-art prediction performance on several benchmark datasets. We also presented several case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results .