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Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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inferred← citeshttps://doi.org/10.1002/9783527645947.ch12openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jcc.26831openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jmr.2266openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201400132openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.202200135openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/prot.24848openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1225openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1429openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1478openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1705openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/wcms.1716openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/7653_2015_46openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4939-6406-2_19openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4939-9752-7_1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-16480-9_24openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-642-34123-6_2openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12539-025-00745-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-323-90264-9.00025-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-49253-2.00017-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/bs.armc.2022.08.008openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2023.107972openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.engappai.2015.06.021openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jmgm.2018.06.005openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00309openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00310openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00418openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00650openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/acsomega.9b04162openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci100473dopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci300604zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci400120bopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci400450hopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci500081mopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ct4004228openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/jm300131xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41596-023-00885-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-27167-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41698-022-00274-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s44386-025-00031-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/srep46710openalex-snapshot
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inferred← citeshttps://doi.org/10.1080/17460441.2021.1909567openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbab225openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbad451openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bib/bbz173openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf040openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btz665openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/database/baaf061openalex-snapshot
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inferred← citeshttps://doi.org/10.1101/2024.12.09.627482openalex-snapshot
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inferred← citeshttps://doi.org/10.1142/s0219633617500742openalex-snapshot
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inferred← citeshttps://doi.org/10.1142/s021972001100577xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3785660openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/11779322241287120openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1471-2105-15-291openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1471-2105-16-s6-s3openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-5-s1-p27openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12859-021-04466-0openalex-snapshot
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inferred← citeshttps://doi.org/10.13140/rg.2.2.30339.73765openalex-snapshot
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inferred← citeshttps://doi.org/10.1360/n032015-00115openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0204999openalex-snapshot
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inferred← citeshttps://doi.org/10.26434/chemrxiv-2022-tcm9vopenalex-snapshot
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inferred← citeshttps://doi.org/10.26434/chemrxiv.11910870openalex-snapshot
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inferred← citeshttps://doi.org/10.26434/chemrxiv.11910870.v2openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fbinf.2022.885983openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fphar.2018.01089openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/biom8010012openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/molecules200610947openalex-snapshot
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inferred← citeshttps://doi.org/10.4018/978-1-5225-0549-5.ch036openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.1803.04465openalex-snapshot
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inferred← citeshttps://doi.org/10.64898/2026.07.27.740774openalex-snapshot
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inferred← citeshttps://doi.org/10.70675/bac5b968z5ac5z406czaa3az3ea700df1ec8openalex-snapshot
Machine learning to understand and engineer the structural kinome
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