DeepPurpose: a deep learning library for drug-target interaction prediction.

Journal: Bioinformatics (Oxford, England)
Published Date: Apr 1, 2021

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.

Authors

  • Kexin Huang
    School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, PR China.
  • Tianfan Fu
    Department of Computer Science, Nanjing University, Nanjing, Jiangsu, China.
  • Lucas M Glass
    Analytics Center of Excellence, IQVIA, Cambridge, Massachusetts, USA.
  • Marinka Zitnik
    Department of Computer Science, Stanford University.
  • Cao Xiao
    University of Washington, Department of Industrial and Systems Engineering, Seattle, USA.
  • Jimeng Sun
    College of Computing Georgia Institute of Technology Atlanta, GA, USA.

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