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Cited by 96Computational Drug Discovery MethodsProtein Structure and DynamicsReceptor Mechanisms and Signaling

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

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Nussinov, Ruth, Zhang, Mingzhen, Liu, Yonglan, Jang, Hyunbum·2023·Drug Discovery Today
DOI 10.1016/j.drudis.2023.103551PMC PMC10238671
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Abstract

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

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  • Numerous areas within the life sciences have experienced considerable impact due to the availability of accurately predicted protein structures, notably structure determination, structure-based drug discovery, and structural bioinformatics.

    “As a result, numerous areas within the life sciences have experienced considerable impact due to the availability of accurately predicted protein structures. The primary domains notably affected include structure determination, structure-based drug discovery, and structural bioinformatics (11–13).”

    From AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences (2024) · cites this paper as [13]

    The Introduction (page unavailable) says AlphaFold’s accurate predictions help solve crystal and cryo-electron microscopy structures and enable broad analyses of structural and functional properties. It also says their performance in drug discovery has been mixed, so the claim overstates the paper’s account of an already considerable impact in that domain. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Cancer-related mutations and chemical modifications can alter protein shapes.

    “Cancer-related mutations and chemical modifications can also alter these shapes.”

    From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [105]

  • AlphaFold predicts a protein's most stable shape but does not model how the protein moves.

    “It predicts a protein’s most stable shape; however, it does not model how the protein moves.”

    From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [105]

  • Drug binding often depends on protein motion, such as small shifts when a compound binds, long-range effects within the protein, hidden pockets that open and close, or changes when proteins form complexes.

    “Drug binding often depends on motion, such as small shifts when a compound binds, long-range effects within the protein, hidden pockets that open and close, or changes when proteins form complexes.”

    From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [105]

  • AlphaFold has limitations for drug discovery, including cancer therapeutics.

    “AlphaFold has been a breakthrough for drug discovery, including cancer therapeutics, but its limitations are well recognized [105,106,107,108].”

    From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [105]

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Cited by

Sorted by citations

Authors
Nussinov, R., Zhang, M., Liu, Y., & Jang, H.
Affiliations
  • Tel Aviv University (IL)
  • Frederick National Laboratory for Cancer Research (US)
  • National Cancer Institute (US)
Corresponding author
Nussinov, Ruth
ORCID
Venue
Drug Discovery Today
Publisher
Elsevier BV
Published
2023-06
Volume
28
Issue
6
Pages
103551
ISSN
1359-6446 (ISSN-L)
DOI
10.1016/j.drudis.2023.103551
PMC
PMC10238671
OpenAlex
W4323926397
Cited by
96
References
13
Citations by year
2026: 24 · 2025: 32 · 2024: 30 · 2023: 10
FWCI
1.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Drug Discovery Today (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • U.S. Department of Health and Human Services (HHSN261201500003I)
  • National Institutes of Health (HHSN261201500003I)
  • National Cancer Institute (Z01BC010440, HHSN261201500003I, Z01BC010441, HHSN261201500003C)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
Keywords
AlphaFold, allosteric drug discovery, protein structure prediction, artificial intelligence in drug discovery, kinases, receptors, rational drug design, drug-binding pocket
MeSH terms
Allosteric Regulation, Allosteric Site, Artificial Intelligence, Proteins, Drug Design, Drug Discovery

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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