Comprehensive survey of recent drug discovery using deep learning

Jintae Kim, Sera Park, Dongbo Min, Wankyu Kim

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Original languageEnglish
Article number9983
JournalInternational Journal of Molecular Sciences
Issue number18
StatePublished - Sep 2021

Bibliographical note

Funding Information:
Funding: WK was funded by National Research Foundation of South Korea (2017M3C9A5028690).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Artificial intelligence-based drug discovery
  • Benchmark tool
  • De novo drug design
  • Deep learning
  • Drug-target interaction
  • Molecular representation
  • Virtual screening


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