Papers › DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction

DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction

19 Apr 2020arXiv:2004.08919archive 2025-07-28

Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun

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.

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