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

• URL: http://arxiv.org/abs/2004.08919v3
• Date: Wed, 9 Dec 2020 23:14:48 GMT
• Title: DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
• Authors: Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun
• Abstract summary: DeepPurpose is a comprehensive and easy-to-use deep learning library for DTI prediction. It supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets.
• Score: 69.7424023336611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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