{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:27:26Z","timestamp":1772119646578,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10822-024-00570-4","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T05:02:31Z","timestamp":1723784551000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FitScore: a fast machine learning-based score for 3D virtual screening enrichment"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7462-5519","authenticated-orcid":false,"given":"Daniel K.","family":"Gehlhaar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5354-1212","authenticated-orcid":false,"given":"Daniel J.","family":"Mermelstein","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"570_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s12551-016-0247-1","author":"NS Pagadala","year":"2017","unstructured":"Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev. https:\/\/doi.org\/10.1007\/s12551-016-0247-1","journal-title":"Biophys Rev"},{"key":"570_CR2","doi-asserted-by":"publisher","unstructured":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucl Acids Res. https:\/\/doi.org\/10.1093\/nar\/28.1.235","DOI":"10.1093\/nar\/28.1.235"},{"key":"570_CR3","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.9b00055","author":"E Wang","year":"2019","unstructured":"Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T (2019) End-point binding free energy calculation with MM\/PBSA and MM\/GBSA: strategies and applications in drug design. Chem Rev. https:\/\/doi.org\/10.1021\/acs.chemrev.9b00055","journal-title":"Chem Rev"},{"key":"570_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/1074-5521(95)90050-0","author":"DK Gehlhaar","year":"1995","unstructured":"Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, Fogel LJ, Freer ST (1995) Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. Chem Biol. https:\/\/doi.org\/10.1016\/1074-5521(95)90050-0","journal-title":"Chem Biol"},{"key":"570_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-019-00327-w","author":"J Li","year":"2019","unstructured":"Li J, Fu A, Zhang L (2019) An overview of scoring functions used for protein\u2013ligand interactions in Molecular Docking. Interdiscip Sci Comput Life Sci. https:\/\/doi.org\/10.1007\/s12539-019-00327-w","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"570_CR6","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.1996.0897","author":"G Jones","year":"1997","unstructured":"Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol. https:\/\/doi.org\/10.1006\/jmbi.1996.0897","journal-title":"J Mol Biol"},{"key":"570_CR7","unstructured":"Schr\u00f6dinger LLC, New York (2023) NY, https:\/\/newsite.schrodinger.com accessed 24 Jan 24 2024"},{"key":"570_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.26862","author":"DK Gehlhaar","year":"2022","unstructured":"Gehlhaar DK, Luty BA, Cheung PP, Litman AH, Owen RM, Rose PW (2022) The Pfizer crystal structure database: an essential tool for structure-based design at Pfizer. J Comp Chem. https:\/\/doi.org\/10.1002\/jcc.26862","journal-title":"J Comp Chem"},{"key":"570_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.24764","author":"GB Goh","year":"2017","unstructured":"Goh GB, Hodas NO, Vishnu (2017) Deep learning for computational chemistry. J Comp Chem. https:\/\/doi.org\/10.1002\/jcc.24764","journal-title":"J Comp Chem"},{"key":"570_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/molecules200713384","author":"LG Ferreira","year":"2015","unstructured":"Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD (2015) Molecular Docking and structure-based drug design strategies. Molecules. https:\/\/doi.org\/10.3390\/molecules200713384","journal-title":"Molecules"},{"key":"570_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-024-00844-x","author":"J Rahman","year":"2024","unstructured":"Rahman J, Newton MAH, Ali ME, Satter A (2024) Distance plus attention for binding affinity prediction. J Chem Inf. https:\/\/doi.org\/10.1186\/s13321-024-00844-x","journal-title":"J Chem Inf"},{"key":"570_CR12","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c00825","author":"R Bhatt","year":"2024","unstructured":"Bhatt R, Koes DR, Durrant JD (2024) CENsible: interpretable insights into small-molecule binding with Context Explanation Networks. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.4c00825","journal-title":"J Chem Inf Model"},{"key":"570_CR13","unstructured":"Anaconda Software Distribution Computer software. Version 2-2.4.0. https:\/\/anaconda.com accessed 10 Dec 2023"},{"key":"570_CR14","doi-asserted-by":"crossref","unstructured":"McKinney W (2010) Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, Austin, United States, June 28-July 3, 51\u201356","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"570_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D (2020) SciPy 1.0: Fundamental algorithms for Scientific Computing in Python. Nat Methods. https:\/\/doi.org\/10.1038\/s41592-019-0686-2","journal-title":"Nat Methods"},{"key":"570_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2649-2","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, del R\u00edo JF, Wiebe M, Peterson P, G\u00e9rard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE (2020) Array programming with NumPy. Nature. https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"570_CR17","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"570_CR18","unstructured":"TensorFlow Large-scale machine learning on heterogeneous systems. https:\/\/www.tensorflow.org accessed 29 Jan 2024"},{"key":"570_CR19","unstructured":"OEChem TK 3.4.0.1. OpenEye, Cadence Molecular Sciences, Santa Fe, NM. http:\/\/www.eyesopen.com accessed 10 Dec 2023"},{"key":"570_CR20","unstructured":"Spicoli TK 1.5.6.1. OpenEye, Cadence Molecular Sciences, Santa Fe, NM. http:\/\/www.eyesopen.com accessed 10 Dec 2023"},{"key":"570_CR21","doi-asserted-by":"publisher","unstructured":"RDKit Open-source cheminformatics. https:\/\/www.rdkit.org. https:\/\/doi.org\/10.5281\/zenodo.591637 accessed 17 July 2024","DOI":"10.5281\/zenodo.591637"},{"key":"570_CR22","doi-asserted-by":"publisher","DOI":"10.1021\/jm100112j","author":"C Bissantz","year":"2010","unstructured":"Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist\u2019s guide to molecular interactions. J Med Chem. https:\/\/doi.org\/10.1021\/jm100112j","journal-title":"J Med Chem"},{"key":"570_CR23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1742-4658.2007.06178.x","author":"A Wlodawer","year":"2008","unstructured":"Wlodawer A, Minor W, Dauter Z, Jaskolski M (2008) Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures. FEBS J. https:\/\/doi.org\/10.1111\/j.1742-4658.2007.06178.x","journal-title":"FEBS J"},{"key":"570_CR24","doi-asserted-by":"publisher","DOI":"10.20544\/HORIZONS.B.04.1.17.P05","author":"V Nasteski","year":"2017","unstructured":"Nasteski V (2017) An overview of the supervised machine learning methods. Horizons. https:\/\/doi.org\/10.20544\/HORIZONS.B.04.1.17.P05","journal-title":"Horizons"},{"key":"570_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-49539-6","author":"S Okada","year":"2019","unstructured":"Okada S, Ohzeki M, Taguchi S (2019) Efficient partition of integer optimization problems with one-hot encoding. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-019-49539-6","journal-title":"Sci Rep"},{"key":"570_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-6631-6_26","author":"N Kosaraju","year":"2023","unstructured":"Kosaraju N, Sankepally SR, Rao KM (2023) Categorical data: need, Encoding, selection of Encoding Method and its Emergence in Machine Learning Models\u2014A practical review study on Heart Disease Prediction dataset using Pearson correlation. Proc Int Conf Data Sci Apps. https:\/\/doi.org\/10.1007\/978-981-19-6631-6_26","journal-title":"Proc Int Conf Data Sci Apps"},{"key":"570_CR27","unstructured":"Keras (2024) https:\/\/github.com\/fchollet\/keras accessed 29"},{"key":"570_CR28","unstructured":"Schr\u00f6dinger Release 2023-1: SiteMap, Schr\u00f6dinger, LLC, New York (2023) NY, https:\/\/newsite.schrodinger.com accessed 24 Jan 2024"},{"key":"570_CR29","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.2001.4452","author":"ML Verdonk","year":"2001","unstructured":"Verdonk ML, Cole JC, Watson P, Gillet V, Willett P (2001) SuperStar: improved knowledge-based interaction fields for protein binding sites. J Mol Biol. https:\/\/doi.org\/10.1006\/jmbi.2001.4452","journal-title":"J Mol Biol"},{"key":"570_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-5931-2_7","volume-title":"Concepts of nonparametric theory","author":"JW Pratt","year":"1981","unstructured":"Pratt JW, Gibbons JD (1981) Kolmogorow-Smirnov two-sample tests. Concepts of nonparametric theory. Springer Series in Statistics. Springer, New York, NY. https:\/\/doi.org\/10.1007\/978-1-4612-5931-2_7"},{"key":"570_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/ICICOS.2017.8276360","author":"SN Endah","year":"2017","unstructured":"Endah SN, Widodo AP, Fariq ML, Nadianada SI, Maulana F (2017) Beyond back-propagation learning for diabetic detection: convergence comparison of gradient descent, momentum and adaptive learning rate. Int Conf Inf Comput Sci. https:\/\/doi.org\/10.1109\/ICICOS.2017.8276360","journal-title":"Int Conf Inf Comput Sci"},{"key":"570_CR32","doi-asserted-by":"publisher","DOI":"10.1021\/jm300687e","author":"MM Mysinger","year":"2012","unstructured":"Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem. https:\/\/doi.org\/10.1021\/jm300687e","journal-title":"J Med Chem"},{"key":"570_CR33","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00712","author":"S Jochen","year":"2019","unstructured":"Jochen S, Flachsenberg F, Rarey M (2019) In need of Bias Control: evaluating Chemical Data for Machine Learning in structure-based virtual screening. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.8b00712","journal-title":"J Chem Inf Model"},{"key":"570_CR34","unstructured":"Schr\u00f6dinger Release 2023-1: LigPrep, Schr\u00f6dinger, LLC, New York (2023) NY, https:\/\/newsite.schrodinger.com accessed 24 Jan 2024"},{"key":"570_CR35","unstructured":"Schr\u00f6dinger Release 2023-1: PrepWizard, Schr\u00f6dinger, LLC, New York (2023) NY, https:\/\/newsite.schrodinger.com accessed 24 Jan 2024"},{"key":"570_CR36","doi-asserted-by":"publisher","DOI":"10.1021\/ci600426e","author":"JF Truchon","year":"2007","unstructured":"Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: Good and Bad Metrics for the early Recognition Problem. J Chem Inf Model. https:\/\/doi.org\/10.1021\/ci600426e","journal-title":"J Chem Inf Model"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-024-00570-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10822-024-00570-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-024-00570-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T07:17:15Z","timestamp":1732605435000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10822-024-00570-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,16]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["570"],"URL":"https:\/\/doi.org\/10.1007\/s10822-024-00570-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4523265\/v1","asserted-by":"object"}]},"ISSN":["0920-654X","1573-4951"],"issn-type":[{"value":"0920-654X","type":"print"},{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,16]]},"assertion":[{"value":"3 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No human or animal subjects were used as part of this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors give their consent for this manuscript to be published.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"29"}}