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The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training\/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+\u2009+\/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jvlehtonen\/overlap-toolkit\">https:\/\/github.com\/jvlehtonen\/overlap-toolkit<\/jats:ext-link>).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Scientific contribution<\/jats:title>\n                <jats:p>This study introduces O-LAP, a C++\/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape\/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s13321-024-00857-6","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T11:02:23Z","timestamp":1723201343000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Building shape-focused pharmacophore models for effective docking screening"],"prefix":"10.1186","volume":"16","author":[{"given":"Paola","family":"Moyano-G\u00f3mez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jukka V.","family":"Lehtonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olli T.","family":"Pentik\u00e4inen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pekka A.","family":"Postila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"issue":"18","key":"857_CR1","doi-asserted-by":"publisher","first-page":"4331","DOI":"10.3390\/ijms20184331","volume":"20","author":"L Pinzi","year":"2019","unstructured":"Pinzi L, Rastelli G (2019) Molecular docking: shifting paradigms in drug discovery. 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