{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T13:45:29Z","timestamp":1791812729141,"version":"4.3.4"},"reference-count":75,"publisher":"American Chemical Society (ACS)","issue":"19","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0025528"],"award-info":[{"award-number":["DE-SC0025528"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35 GM151129"],"award-info":[{"award-number":["R35 GM151129"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting protein\u2013ligand binding is a central challenge in computational drug discovery, and while machine learning (ML) and cofolding methods have advanced rapidly, their ability to generalize beyond training or parametrization regimes remains insufficiently understood. DNA-encoded libraries (DELs) enable ultralarge screening of billions of molecules simultaneously, providing a useful testbed for evaluating these approaches at scale. A recent NeurIPS competition revealed that even top-performing ML models trained on DEL data failed at generalizing to out-of-distribution (OOD) chemical space. We investigated whether integrating structural modeling could bridge this generalization gap. We systematically assessed state-of-the-art ML, docking, and cofolding methods, including Schro\u0308dinger Glide, Rosetta GALigandDock, and Boltz-2 with three biologically diverse protein targets screened against libraries containing multiple DEL synthesis formats. While ML excels in-distribution, OOD hit discrimination is dependent on both the target and ligand context, with no single method consistently dominating. These findings demonstrate that benchmark performance alone is insufficient to predict OOD performance, highlighting the need for system-dependent evaluation of binding prediction methods. We provide an open-source package for assessing protein\u2013ligand prediction methods and analyzing high-throughput screening data: DEL-iver.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01611","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:03:19Z","timestamp":1789754599000},"page":"12360-12374","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Generalizability\nof Machine Learning\nand Physics-Based Methods with DNA-Encoded Libraries"],"prefix":"10.1021","volume":"66","author":[{"given":"Marissa","family":"Dolorfino","sequence":"first","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]},{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48109"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Santos Perez","sequence":"additional","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1034-0223","authenticated-orcid":true,"given":"Shu-Hang","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"McCarty","sequence":"additional","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew J.","family":"O\u2019Meara","sequence":"additional","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]},{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48109"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1327-8541","authenticated-orcid":true,"given":"Terra","family":"Sztain","sequence":"additional","affiliation":[{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48104"]},{"name":"University of Michigan , , , ,","place":["Ann Arbor, Michigan, United States, 48109"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"issue":"6","key":"2026101208073379000_cit1","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","article-title":"Principles of Early\nDrug Discovery","volume":"162","author":"Hughes","year":"2011","journal-title":"Br. J. Pharmacol."},{"issue":"1","key":"2026101208073379000_cit2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37572-z","article-title":"Improving the Generalizability of\nProtein-Ligand Binding Predictions with AI-Bind","volume":"14","author":"Chatterjee","year":"2023","journal-title":"Nat. Commun."},{"issue":"2","key":"2026101208073379000_cit3","doi-asserted-by":"publisher","first-page":"146","DOI":"10.2174\/157340911795677602","article-title":"Molecular\nDocking:\nA Powerful Approach for Structure-Based Drug Discovery","volume":"7","author":"Meng","year":"2011","journal-title":"Curr. Comput.-Aided Drug Des."},{"issue":"1","key":"2026101208073379000_cit4","doi-asserted-by":"publisher","first-page":"10","DOI":"10.2174\/157340911793743547","article-title":"Recent Advances in Ligand-Based Drug\nDesign: Relevance\nand Utility of the Conformationally Sampled Pharmacophore Approach","volume":"7","author":"Acharya","year":"2011","journal-title":"Curr. Comput.-Aided Drug Des."},{"key":"2026101208073379000_cit5","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.1016\/j.csbj.2022.06.004","article-title":"A Brief Review\nof Protein-Ligand Interaction Prediction","volume":"20","author":"Zhao","year":"2022","journal-title":"Comput.\nStruct. Biotechnol. J."},{"issue":"7873","key":"2026101208073379000_cit6","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly Accurate Protein\nStructure Prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"issue":"8016","key":"2026101208073379000_cit7","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate Structure Prediction of\nBiomolecular Interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2026101208073379000_cit8","doi-asserted-by":"crossref","unstructured":"Passaro, S.; Corso, G.; Wohlwend, J.; Reveiz, M.; Thaler, S.; Somnath, V. R.; Getz, N.; Portnoi, T.; Roy, J.; Stark, H.; Kwabi-Addo, D.; Beaini, D.; Jaakkola, T.; Barzilay, R.\n          Boltz-2: Towards Accurate and Efficient Binding Affinity\nPrediction\n          bioRxiv\n          2025\n          10.1101\/2025.06.14.659707.","DOI":"10.1101\/2025.06.14.659707"},{"key":"2026101208073379000_cit9","unstructured":"Corso, G.; Sta\u0308rk, H.; Jing, B.; Barzilay, R.; Jaakkola, T.\n          DiffDock: Diffusion Steps,\nTwists, and Turns for Molecular Docking\n          2022\n          http:\/\/arxiv.org\/abs\/2210.01776."},{"issue":"2","key":"2026101208073379000_cit10","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s42256-024-00792-z","article-title":"State-Specific\nProtein\u2013ligand Complex Structure Prediction with a Multiscale\nDeep Generative Model","volume":"6","author":"Qiao","year":"2024","journal-title":"Nat. Mach. 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