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

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

Research output: Contribution to journalArticlepeer-review

Abstract

SUMMARY: 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 DL 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. AVAILABILITY AND IMPLEMENTATION: https://github.com/kexinhuang12345/DeepPurpose. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)5545-5547
Number of pages3
JournalBioinformatics (Oxford, England)
Volume36
Issue number22-23
Early online dateDec 4 2020
DOIs
StatePublished - Apr 1 2021

ASJC Scopus subject areas

  • Computational Mathematics
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability
  • Computer Science Applications
  • Computational Theory and Mathematics

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