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Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we present a novel coarse-grained potential defined by a 3D joint probability distribution function that only depends on the pairwise orientation and position between protein backbone and ligand atoms. Despite its extreme simplicity, our approach yields very competitive results with the state-of-the-art scoring functions, especially in docking and screening tasks. For example, we observed a twofold improvement in the median 5% enrichment factor on the DUD-E benchmark compared to Autodock Vina results. Moreover, our results prove that a coarse sidechain-free potential is sufficient for a very successful docking pose prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availabilityand implementation<\/jats:title><jats:p>The standalone version of KORP-PL with the corresponding tests and benchmarks are available at https:\/\/team.inria.fr\/nano-d\/korp-pl\/ and https:\/\/chaconlab.org\/modeling\/korp-pl.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa748","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T12:40:07Z","timestamp":1597754407000},"page":"943-950","source":"Crossref","is-referenced-by-count":24,"title":["KORP-PL: a coarse-grained knowledge-based scoring function for protein\u2013ligand interactions"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3168-4826","authenticated-orcid":false,"given":"Maria","family":"Kadukova","sequence":"first","affiliation":[{"name":"Univ. 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Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK , 38000 Grenoble, France"},{"name":"Computational Biology Laboratory, Centro de Ci\u00eancias Computacionais, Universidade Federal do Rio Grande \u2013 FURG , Rio Grande, RS 96201-090, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"Chac\u00f3n","sequence":"additional","affiliation":[{"name":"Department of Biological Physical Chemistry, Rocasolano Institute of Physical Chemistry C.S.I.C , Madrid 28006, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1903-7220","authenticated-orcid":false,"given":"Sergei","family":"Grudinin","sequence":"additional","affiliation":[{"name":"Univ. 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