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The Pharmacophore Kernel for Virtual Screening with Support Vector Machines
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inferred← citeshttps://doi.org/10.1002/9781118131411.ch6openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4939-8639-2_12openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-62703-107-3_9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-540-72903-7_10openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-642-01184-9_3openalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci600397popenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci700350nopenalex-snapshot
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inferred← citeshttps://doi.org/10.1021/ci800079xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btaf208openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btp433openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/bioinformatics/btq140openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8982993openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/bmei.2015.7401557openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/grc.2014.6982869openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/1401890.1401961openalex-snapshot
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inferred← citeshttps://doi.org/10.1145/3107411.3107473openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1471-2105-9-363openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1752-153x-3-s1-p11openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-1-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-2-2openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-2-s1-p36openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/1758-2946-3-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12859-020-3378-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13321-015-0110-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13321-024-00830-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0104586openalex-snapshot
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inferred← citeshttps://doi.org/10.1517/17460441.2014.866943openalex-snapshot
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inferred← citeshttps://doi.org/10.2174/138620708785739899openalex-snapshot
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inferred← citeshttps://doi.org/10.22271/allresearch.2021.v7.i4a.8455openalex-snapshot
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inferred← citeshttps://doi.org/10.3233/978-1-60750-692-8-3openalex-snapshot
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inferred← citeshttps://doi.org/10.3233/ida-163343openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fenvs.2015.00080openalex-snapshot
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inferred← citeshttps://doi.org/10.34040/ijcb.1.1.2012.12openalex-snapshot
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inferred← citeshttps://doi.org/10.4018/978-1-60960-818-7.ch315openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.0709.3931openalex-snapshot
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inferred← citeshttps://doi.org/10.48550/arxiv.1902.10042openalex-snapshot
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inferred← citeshttps://doi.org/10.5120/16440-5878openalex-snapshot
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inferred← citeshttps://doi.org/10.5451/unibas-006418985openalex-snapshot
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inferred← citeshttps://doi.org/10.55041/ijsrem35793openalex-snapshot
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inferred← citeshttps://doi.org/10.5935/1984-6835.20120055openalex-snapshot
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inferred← citeshttps://doi.org/10.6084/m9.figshare.26697593openalex-snapshot
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inferred← citeshttps://doi.org/10.70675/219dc6f8z5151z49e1zab5bz19f8c2ffeb4fopenalex-snapshot
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