{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:47:54Z","timestamp":1782312474919,"version":"3.54.5"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92370130"],"award-info":[{"award-number":["92370130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-025-00993-0","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T05:03:37Z","timestamp":1739423017000},"page":"509-520","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Benchmarking AI-powered docking methods from the perspective of virtual screening"],"prefix":"10.1038","volume":"7","author":[{"given":"Shukai","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2783-5529","authenticated-orcid":false,"given":"Chao","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7107-7481","authenticated-orcid":false,"given":"Huiyong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2868-6653","authenticated-orcid":false,"given":"Heng","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huifeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenggong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silong","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yafeng","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-3667","authenticated-orcid":false,"given":"Huanxiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-2580","authenticated-orcid":false,"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0999-8802","authenticated-orcid":false,"given":"Yu","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"993_CR1","doi-asserted-by":"publisher","first-page":"146","DOI":"10.2174\/157340911795677602","volume":"7","author":"XY Meng","year":"2011","unstructured":"Meng, X. Y., Zhang, H. X., Mezei, M. & Cui, M. Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des. 7, 146\u2013157 (2011).","journal-title":"Curr. Comput. Aided Drug Des."},{"key":"993_CR2","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1038\/nprot.2016.051","volume":"11","author":"S Forli","year":"2016","unstructured":"Forli, S. et al. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc. 11, 905\u2013919 (2016).","journal-title":"Nat. Protoc."},{"key":"993_CR3","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.2174\/1568026614666140929124445","volume":"14","author":"E Lionta","year":"2014","unstructured":"Lionta, E., Spyrou, G., Vassilatis, D. K. & Cournia, Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr. Top. Med. Chem. 14, 1923\u20131938 (2014).","journal-title":"Curr. Top. Med. Chem."},{"key":"993_CR4","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1021\/jm0306430","volume":"47","author":"RA Friesner","year":"2004","unstructured":"Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739\u20131749 (2004).","journal-title":"J. Med. Chem."},{"key":"993_CR5","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1002\/cmdc.201200331","volume":"7","author":"H Zhao","year":"2012","unstructured":"Zhao, H., Huang, D. & Caflisch, A. Discovery of tyrosine kinase inhibitors by docking into an inactive kinase conformation generated by molecular dynamics. ChemMedChem 7, 1983\u20131990 (2012).","journal-title":"ChemMedChem"},{"key":"993_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003571","volume":"10","author":"S Ruiz-Carmona","year":"2014","unstructured":"Ruiz-Carmona, S. et al. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput. Biol. 10, e1003571 (2014).","journal-title":"PLoS Comput. Biol."},{"key":"993_CR7","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1021\/jm020406h","volume":"46","author":"AN Jain","year":"2003","unstructured":"Jain, A. N. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 46, 499\u2013511 (2003).","journal-title":"J. Med. Chem."},{"key":"993_CR8","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s12551-016-0247-1","volume":"9","author":"NS Pagadala","year":"2017","unstructured":"Pagadala, N. S., Syed, K. & Tuszynski, J. Software for molecular docking: a review. Biophys. Rev. 9, 91\u2013102 (2017).","journal-title":"Biophys. Rev."},{"key":"993_CR9","doi-asserted-by":"publisher","first-page":"12964","DOI":"10.1039\/C6CP01555G","volume":"18","author":"Z Wang","year":"2016","unstructured":"Wang, Z. et al. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys. 18, 12964\u201312975 (2016).","journal-title":"Phys. Chem. Chem. Phys."},{"key":"993_CR10","unstructured":"Yu, Y., Lu, S., Gao, Z., Zheng, H. & Ke, G. Proc. ICLR 2023\u2014Machine Learning for Drug Discovery Workshop (ICLR, 2023)."},{"key":"993_CR11","doi-asserted-by":"publisher","first-page":"10691","DOI":"10.1021\/acs.jmedchem.2c00991","volume":"65","author":"C Shen","year":"2022","unstructured":"Shen, C. et al. Boosting protein-ligand binding pose prediction and virtual screening based on residue-atom distance likelihood potential and graph transformer. J. Med. Chem. 65, 10691\u201310706 (2022).","journal-title":"J. Med. Chem."},{"key":"993_CR12","doi-asserted-by":"publisher","first-page":"8129","DOI":"10.1039\/D3SC02044D","volume":"14","author":"C Shen","year":"2023","unstructured":"Shen, C. et al. A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem. Sci. 14, 8129\u20138146 (2023).","journal-title":"Chem. Sci."},{"key":"993_CR13","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1038\/s42256-024-00849-z","volume":"6","author":"DH Cao","year":"2024","unstructured":"Cao, D. H. et al. Generic protein-ligand interaction scoring by integrating physical prior knowledge and data augmentation modelling. Nat. Mach. Intell. 6, 688\u2013700 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"993_CR14","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1039\/D1SC06946B","volume":"13","author":"S Moon","year":"2022","unstructured":"Moon, S., Zhung, W., Yang, S., Lim, J. & Kim, W. Y. PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. Chem. Sci. 13, 3661\u20133673 (2022).","journal-title":"Chem. Sci."},{"key":"993_CR15","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1039\/D3DD00149K","volume":"3","author":"S Moon","year":"2024","unstructured":"Moon, S., Hwang, S. Y., Lim, J. & Kim, W. Y. PIGNet2: a versatile deep learning-based protein-ligand interaction prediction model for binding affinity scoring and virtual screening. Digit. Discov. 3, 287\u2013299 (2024).","journal-title":"Digit. Discov."},{"key":"993_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/srep46710","volume":"7","author":"M Wojcikowski","year":"2017","unstructured":"Wojcikowski, M., Ballester, P. J. & Siedlecki, P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci. Rep. 7, 46710 (2017).","journal-title":"Sci. Rep."},{"key":"993_CR17","doi-asserted-by":"publisher","first-page":"8424","DOI":"10.3390\/ijms21228424","volume":"21","author":"Y Kwon","year":"2020","unstructured":"Kwon, Y., Shin, W. H., Ko, J. & Lee, J. AK-Score: accurate protein-ligand binding affinity prediction using an ensemble of 3D-convolutional neural networks. Int. J. Mol. Sci. 21, 8424 (2020).","journal-title":"Int. J. Mol. Sci."},{"key":"993_CR18","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1039\/D3SC05552C","volume":"15","author":"H Cai","year":"2024","unstructured":"Cai, H. et al. CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training. Chem. Sci. 15, 1449\u20131471 (2024).","journal-title":"Chem. Sci."},{"key":"993_CR19","unstructured":"Corso, G., St\u00e4rk, H., Jing, B., Barzilay, R. & Jaakkola, T. S. Proc. Eleventh International Conference on Learning Representations (ICLR, 2023)."},{"key":"993_CR20","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1038\/s43588-023-00511-5","volume":"3","author":"X Zhang","year":"2023","unstructured":"Zhang, X. et al. Efficient and accurate large library ligand docking with KarmaDock. Nat. Comput. Sci. 3, 789\u2013804 (2023).","journal-title":"Nat. Comput. Sci."},{"key":"993_CR21","doi-asserted-by":"publisher","first-page":"363","DOI":"10.5582\/ddt.2023.01063","volume":"17","author":"Y Li","year":"2023","unstructured":"Li, Y., Li, L., Wang, S. & Tang, X. EQUIBIND: a geometric deep learning-based protein-ligand binding prediction method. Drug Discov. Ther. 17, 363\u2013364 (2023).","journal-title":"Drug Discov. Ther."},{"key":"993_CR22","doi-asserted-by":"publisher","first-page":"8446","DOI":"10.1021\/acs.jctc.3c00273","volume":"19","author":"T Dong","year":"2023","unstructured":"Dong, T., Yang, Z., Zhou, J. & Chen, C. Y. Equivariant flexible modeling of the protein-ligand binding pose with geometric deep learning. J. Chem. Theory Comput. 19, 8446\u20138459 (2023).","journal-title":"J. Chem. Theory Comput."},{"key":"993_CR23","doi-asserted-by":"crossref","unstructured":"Lu, W. et al. TANKBind: trigonometry-aware neural networks for drug-protein binding structure prediction. In Advances in Neural Information Processing Systems 35 (eds Koyejo, S. et al.) 7236\u20137249 (NeurIPS, 2022).","DOI":"10.52202\/068431-0525"},{"key":"993_CR24","unstructured":"Geng, M. et al. Proc. Eleventh International Conference on Learning Representations (ICLR, 2023)."},{"key":"993_CR25","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1021\/acs.accounts.4c00093","volume":"57","author":"XJ Zhang","year":"2024","unstructured":"Zhang, X. J. et al. Advancing ligand docking through deep learning: challenges and prospects in virtual screening. Acc. Chem. Res. 57, 1500\u20131509 (2024).","journal-title":"Acc. Chem. Res."},{"key":"993_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37572-z","volume":"14","author":"A Chatterjee","year":"2023","unstructured":"Chatterjee, A. et al. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nat. Commun. 14, 1989 (2023).","journal-title":"Nat. Commun."},{"key":"993_CR27","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1021\/acs.jcim.8b00712","volume":"59","author":"J Sieg","year":"2019","unstructured":"Sieg, J., Flachsenberg, F. & Rarey, M. In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. J. Chem. Inf. Model. 59, 947\u2013961 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR28","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1038\/s42256-022-00605-1","volume":"5","author":"PZ Bai","year":"2023","unstructured":"Bai, P. Z., Miljkovic, F., John, B. & Lu, H. P. Interpretable bilinear attention network with domain adaptation improves drug-target prediction. Nat. Mach. Intell. 5, 126\u2013136 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"993_CR29","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s43588-024-00627-2","volume":"4","author":"T Siebenmorgen","year":"2024","unstructured":"Siebenmorgen, T. et al. MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery. Nat. Comput. Sci. 4, 367\u2013378 (2024).","journal-title":"Nat. Comput. Sci."},{"key":"993_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-40160-2","volume":"13","author":"PC Agu","year":"2023","unstructured":"Agu, P. C. et al. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep. 13, 13398 (2023).","journal-title":"Sci. Rep."},{"key":"993_CR31","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1021\/ci100031x","volume":"50","author":"PC Hawkins","year":"2010","unstructured":"Hawkins, P. C., Skillman, A. G., Warren, G. L., Ellingson, B. A. & Stahl, M. T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J. Chem. Inf. Model. 50, 572\u2013584 (2010).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR32","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/acs.jcim.7b00505","volume":"57","author":"NO Friedrich","year":"2017","unstructured":"Friedrich, N. O. et al. Benchmarking commercial conformer ensemble generators. J. Chem. Inf. Model. 57, 2719\u20132728 (2017).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR33","doi-asserted-by":"publisher","first-page":"3130","DOI":"10.1039\/D3SC04185A","volume":"15","author":"M Buttenschoen","year":"2024","unstructured":"Buttenschoen, M., Morris, G. M. & Deane, C. M. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chem. Sci. 15, 3130\u20133139 (2024).","journal-title":"Chem. Sci."},{"key":"993_CR34","doi-asserted-by":"publisher","first-page":"6582","DOI":"10.1021\/jm300687e","volume":"55","author":"MM Mysinger","year":"2012","unstructured":"Mysinger, M. M., Carchia, M., Irwin, J. J. & Shoichet, B. K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem. 55, 6582\u20136594 (2012).","journal-title":"J. Med. Chem."},{"key":"993_CR35","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1021\/ci400115b","volume":"53","author":"MR Bauer","year":"2013","unstructured":"Bauer, M. R., Ibrahim, T. M., Vogel, S. M. & Boeckler, F. M. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0-a public library of challenging docking benchmark sets. J. Chem. Inf. Model. 53, 1447\u20131462 (2013).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR36","doi-asserted-by":"publisher","first-page":"e0220113","DOI":"10.1371\/journal.pone.0220113","volume":"14","author":"L Chen","year":"2019","unstructured":"Chen, L. et al. Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening. PLoS ONE 14, e0220113 (2019).","journal-title":"PLoS ONE"},{"key":"993_CR37","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1021\/acs.jcim.0c00598","volume":"61","author":"RM Stein","year":"2021","unstructured":"Stein, R. M. et al. Property-unmatched decoys in docking benchmarks. J. Chem. Inf. Model. 61, 699\u2013714 (2021).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR38","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","volume":"60","author":"VK Tran-Nguyen","year":"2020","unstructured":"Tran-Nguyen, V. K., Jacquemard, C. & Rognan, D. LIT-PCBA: an unbiased data set for machine learning and virtual screening. J. Chem. Inf. Model. 60, 4263\u20134273 (2020).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR39","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer, S. G. & Baumann, K. Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. J. Chem. Inf. Model. 49, 169\u2013184 (2009).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR40","doi-asserted-by":"publisher","first-page":"6065","DOI":"10.1021\/acs.jcim.0c00675","volume":"60","author":"JJ Irwin","year":"2020","unstructured":"Irwin, J. J. et al. ZINC20\u2014a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065\u20136073 (2020).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR41","doi-asserted-by":"publisher","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","volume":"51","author":"UniProt C.","year":"2023","unstructured":"UniProt C. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51, D523\u2013D531 (2023).","journal-title":"Nucleic Acids Res."},{"key":"993_CR42","doi-asserted-by":"publisher","first-page":"D198","DOI":"10.1093\/nar\/gkl999","volume":"35","author":"T Liu","year":"2007","unstructured":"Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, D198\u2013D201 (2007).","journal-title":"Nucleic Acids Res."},{"key":"993_CR43","doi-asserted-by":"crossref","unstructured":"Kim, S. et al. PubChem 2023 update. Nucleic Acids Res. 51, D1373\u2013D1380 (2023).","DOI":"10.1093\/nar\/gkac956"},{"key":"993_CR44","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","volume":"52","author":"B Zdrazil","year":"2024","unstructured":"Zdrazil, B. et al. The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 52, D1180\u2013D1192 (2024).","journal-title":"Nucleic Acids Res."},{"key":"993_CR45","doi-asserted-by":"publisher","first-page":"D475","DOI":"10.1093\/nar\/gks1200","volume":"41","author":"PW Rose","year":"2013","unstructured":"Rose, P. W. et al. The RCSB Protein Data Bank: new resources for research and education. Nucleic Acids Res. 41, D475\u2013D482 (2013).","journal-title":"Nucleic Acids Res."},{"key":"993_CR46","doi-asserted-by":"publisher","first-page":"116227","DOI":"10.1016\/j.ejmech.2024.116227","volume":"268","author":"S Chen","year":"2024","unstructured":"Chen, S. et al. Identification of 3-aryl-5-methyl-isoxazole-4-carboxamide derivatives and analogs as novel HIF-2alpha agonists through docking-based virtual screening and structural modification. Eur. J. Med. Chem. 268, 116227 (2024).","journal-title":"Eur. J. Med. Chem."},{"key":"993_CR47","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1021\/jm030580l","volume":"47","author":"R Wang","year":"2004","unstructured":"Wang, R., Fang, X., Lu, Y. & Wang, S. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem. 47, 2977\u20132980 (2004).","journal-title":"J. Med. Chem."},{"key":"993_CR48","doi-asserted-by":"publisher","first-page":"5381","DOI":"10.1021\/acs.jcim.4c00621","volume":"64","author":"C Shen","year":"2024","unstructured":"Shen, C. et al. DrugFlow: an AI-driven one-stop platform for innovative drug discovery. J. Chem. Inf. Model. 64, 5381\u20135391 (2024).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR49","doi-asserted-by":"publisher","first-page":"101850","DOI":"10.1016\/j.xcrp.2024.101850","volume":"5","author":"SK Gu","year":"2024","unstructured":"Gu, S. K. et al. AMGC is a multiple-task graph neutral network for epigenetic target profiling. Cell Rep. Phys. Sci. 5, 101850 (2024).","journal-title":"Cell Rep. Phys. Sci."},{"key":"993_CR50","doi-asserted-by":"publisher","first-page":"7274","DOI":"10.1021\/acs.jcim.3c00884","volume":"63","author":"K Degn","year":"2023","unstructured":"Degn, K., Beltrame, L., Tiberti, M. & Papaleo, E. PDBminer to find and annotate protein structures for computational analysis. J. Chem. Inf. Model. 63, 7274\u20137281 (2023).","journal-title":"J. Chem. Inf. Model."},{"key":"993_CR51","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s10822-013-9644-8","volume":"27","author":"GM Sastry","year":"2013","unstructured":"Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R. & Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221\u2013234 (2013).","journal-title":"J. Comput. Aided Mol. Des."},{"key":"993_CR52","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1021\/ct100578z","volume":"7","author":"MH Olsson","year":"2011","unstructured":"Olsson, M. H., Sondergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7, 525\u2013537 (2011).","journal-title":"J. Chem. Theory Comput."},{"key":"993_CR53","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s13321-015-0078-2","volume":"7","author":"M Wojcikowski","year":"2015","unstructured":"Wojcikowski, M., Zielenkiewicz, P. & Siedlecki, P. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J. Cheminform. 7, 26 (2015).","journal-title":"J. Cheminform."},{"key":"993_CR54","doi-asserted-by":"publisher","unstructured":"Gu, S. VSDS-VD: Benchmarking AI-powered docking methods from the perspective of virtual screening. Zenodo https:\/\/doi.org\/10.5281\/zenodo.13684010 (2024).","DOI":"10.5281\/zenodo.13684010"},{"key":"993_CR55","doi-asserted-by":"publisher","unstructured":"Gu, S. Code for \u2018Benchmarking AI-powered docking methods from the perspective of virtual screening\u2019. Zenodo https:\/\/doi.org\/10.5281\/zenodo.14649209 (2024).","DOI":"10.5281\/zenodo.14649209"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-00993-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-00993-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-00993-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:05:42Z","timestamp":1782309942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-025-00993-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":55,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["993"],"URL":"https:\/\/doi.org\/10.1038\/s42256-025-00993-0","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]},"assertion":[{"value":"24 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}