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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\u2010ligand interactions between 296 proteins spanning\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    '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\u2010based 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\u2010positive rate to false\u2010positive rate. This work indicates that advances in modeling protein\u2010ligand interactions, particularly using machine learning\u2010based approaches, are needed to better harness AlphaFold2 for drug discovery.\n                  <\/jats:p>","DOI":"10.15252\/msb.202211081","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T05:15:38Z","timestamp":1662441338000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":292,"title":["Benchmarking AlphaFold\u2010enabled molecular docking predictions for antibiotic discovery"],"prefix":"10.1038","volume":"18","author":[{"given":"Felix","family":"Wong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7936-4442","authenticated-orcid":false,"given":"Aarti","family":"Krishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica J","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4463-326X","authenticated-orcid":false,"given":"Hannes","family":"St\u00e4rk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3800-0714","authenticated-orcid":false,"given":"Abigail L","family":"Manson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashlee M","family":"Earl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommi","family":"Jaakkola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-8246","authenticated-orcid":false,"given":"James J","family":"Collins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"BFMSB202211081_CR1","doi-asserted-by":"crossref","first-page":"18477","DOI":"10.1073\/pnas.2000585117","volume":"117","author":"YO Adeshina","year":"2020","unstructured":"Adeshina YO, Deeds EJ, Karanicolas J (2020) Machine learning classification can reduce false positives in structure\u2010based virtual screening. 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