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Cited by 295Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

Deep generative molecular design reshapes drug discovery

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Zeng, Xiangxiang, Wang, Fei, Luo, Yuan, Kang, Seung-gu, Tang, Jian, Lightstone, Felice C., Fang, Evandro F., Cornell, Wendy +2 more·2022·Cell Reports Medicine
DOI 10.1016/j.xcrm.2022.100794PMC PMC9797947
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Abstract

Recent advances and accomplishments of artificial intelligence (AI) and deep generative models have established their usefulness in medicinal applications, especially in drug discovery and development. To correctly apply AI, the developer and user face questions such as which protocols to consider, which factors to scrutinize, and how the deep generative models can integrate the relevant disciplines. This review summarizes classical and newly developed AI approaches, providing an updated and accessible guide to the broad computational drug discovery and development community. We introduce deep generative models from different standpoints and describe the theoretical frameworks for representing chemical and biological structures and their applications. We discuss the data and technical challenges and highlight future directions of multimodal deep generative models for accelerating drug discovery.

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Sorted by citations

Authors
Zeng, X., Wang, F., Luo, Y., Kang, S.-G., Tang, J., Lightstone, F. C., Fang, E. F., Cornell, W., Nussinov, R., & Cheng, F.
Affiliations
  • Hunan University (CN)
  • Cornell University (US)
  • Northwestern University (US)
  • IBM Research - Thomas J. Watson Research Center (US)
  • HEC Montréal (CA)
  • Lawrence Livermore National Laboratory (US)
  • University of Oslo (NO)
  • Tel Aviv University (IL)
  • Cleveland Clinic (US)
Corresponding author
Cheng, Feixiong
ORCID
Venue
Cell Reports Medicine
Publisher
Elsevier BV
Published
2022-12
Volume
3
Issue
12
Pages
100794
ISSN
2666-3791 (ISSN-L)
DOI
10.1016/j.xcrm.2022.100794
PMC
PMC9797947
OpenAlex
W4307447012
Cited by
295
References
12
Citations by year
2026: 73 · 2025: 102 · 2024: 92 · 2023: 27 · 2022: 1
FWCI
4.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell Reports Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, public-domain)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
Funders
  • Cleveland Clinic
  • National Institutes of Health (HHSN261201500003I)
  • National Cancer Institute (HHSN261201500003I, HHSN261201500003C)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
deep generative models, generative molecular design, computational drug discovery, artificial intelligence in drug development, chemical structure representation, multimodal generative models
MeSH terms
Artificial Intelligence, Drug Discovery

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