  Accurate prediction of drug-target interactions (DTI) is crucial for drug
discovery. Recently, deep learning (DL) models for show promising performance
for DTI prediction. However, these models can be difficult to use for both
computer scientists entering the biomedical field and bioinformaticians with
limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use
deep learning library for DTI prediction. DeepPurpose supports training of
customized DTI prediction models by implementing 15 compound and protein
encoders and over 50 neural architectures, along with providing many other
useful features. We demonstrate state-of-the-art performance of DeepPurpose on
several benchmark datasets.
