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

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

2020-04-19Drug Discovery Prediction Deep Learning

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.

Results

Task	Dataset	Metric	Value	Model
Drug Discovery	DAVIS-DTA	CI	0.881	DeepPurpose
Drug Discovery	DAVIS-DTA	MSE	0.242	DeepPurpose
Drug Discovery	KIBA	CI	0.872	DeepPurpose
Drug Discovery	KIBA	MSE	0.178	DeepPurpose

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