Artificial intelligence in drug discovery: applications and techniques.

Journal: Briefings in bioinformatics
Published Date:

Abstract

Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.

Authors

  • Jianyuan Deng
    Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States.
  • Zhibo Yang
    Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA. Electronic address: Zhibo.Yang@ou.edu.
  • Iwao Ojima
    Institute of Chemical Biology and Drug Discovery, Stony Brook University Stony Brook NY USA iwao.ojima@stonybrook.edu.
  • Dimitris Samaras
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
  • Fusheng Wang
    Stony Brook University, Stony Brook, NY.