DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening
Kexin Huang, Tianfan Fu, Lucas M. Glass, Marinka Žitnik, Cao Xiao, Jimeng Sun · arXiv (Cornell University) · 2020
Accurate prediction of drug-target interactions (DTI) enables drug discovery tasks, including virtual screening and drug repurposing, which can shorten the time to identify promising drug candidates and provide cures to patients. Recently, there is a growing number of research that developed deep learning (DL) models for DTI. Despite their superior performance, these research models are difficult to use in real drug discovery practice due to the complexity of deploying the research code as well as the restricted data formatting, model capacity, and evaluation setting. We present DeepPurpose, a comprehensive and easy-to-use software toolkit for DL based drug-target interaction (DTI) prediction with applications to drug screening and repurposing. The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes. Further, computer scientists can use DeepPurpose to train customized DTI prediction models with 15 drug and 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 a study on drug repurposing for COVID-19, where promising drug candidates currently investigated in clinical trials are ranked high in DeepPurpose predictions.