Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Open accessAbstract
Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli 's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery.
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There are advances in computationally modeling how small molecules and proteins interact via docking.
“Within the chemoinformatics space, there are advances in computationally modeling how small molecules and proteins interact via docking.157–159”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [158]
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- Authors
- Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J.
- Affiliations
- Broad Institute (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Collins, James J
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2022-09-01
- Volume
- 18
- Issue
- 9
- Pages
- e11081-e11081
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC9446081
- OpenAlex
- W4294719209
- Cited by
- 284
- References
- 52
- Citations by year
- 2026: 61 · 2025: 85 · 2024: 70 · 2023: 60 · 2022: 8
- FWCI
- 30.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Broad Institute of MIT and Harvard
- Flu Lab, LLC
- HHS¦NIH¦National Institute of Allergy and Infectious Diseases (U19AI110818, R01‐AI146194)
- HHS¦NIH¦National Institute of General Medical Sciences (P41‐GM103311)
- James S. McDonnell Foundation
- Rosamund Zander and Hansjorg Wyss
- Sea Grape Foundation
- Swiss National Science Foundation (SNSF_ 203071)
- The Audacious Project
- 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 › vaccines and immunoinformatics approaches
- Keywords
- AlphaFold2, molecular docking, protein-ligand interactions, antibiotic discovery, antibacterial agents, machine learning, drug target prediction, benchmarking
- MeSH terms
- Anti-Bacterial Agents, Ligands, Protein Binding, Proteins, Benchmarking, Molecular Docking Simulation
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.
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