{"id":"https://openalex.org/W7160290761","doi":"https://doi.org/10.48550/arxiv.2605.01681","title":"Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN","display_name":"Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN","publication_year":2026,"publication_date":"2026-05-03","ids":{"openalex":"https://openalex.org/W7160290761","doi":"https://doi.org/10.48550/arxiv.2605.01681"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01681","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.01681","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123926602","display_name":"Youssef Abo-Dahab","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abo-Dahab, Youssef","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135389680","display_name":"Xiaoiang Xiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang, Xiaoiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chun, Joanne","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chun, Joanne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Zhao, Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.8888999819755554,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.8888999819755554,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.025100000202655792,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.021400000900030136,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7967000007629395},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5884000062942505},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5809000134468079},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4341999888420105},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42809998989105225}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7967000007629395},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7411999702453613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7300000190734863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.63919997215271},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5884000062942505},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4341999888420105},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.35249999165534973},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3255000114440918},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3188999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01681","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.01681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01681","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Virtual":[0],"screening":[1],"performance":[2],"depends":[3],"heavily":[4],"on":[5,29,41,83,111,162],"the":[6,42,94,127,135,152,186],"chosen":[7],"docking":[8,84,170],"and":[9,18,54,59,69,77,175],"scoring":[10],"methods.":[11],"Recent":[12],"AI-based":[13],"tools":[14],"such":[15,114],"as":[16,93,115],"DiffDock":[17,60],"NMDN":[19],"have":[20],"reported":[21],"strong":[22],"benchmark":[23],"results,":[24],"but":[25,123],"their":[26],"practical":[27,188],"utility":[28],"realistic,":[30],"experimentally-derived":[31],"datasets":[32],"remains":[33],"unclear.":[34],"Here":[35],"we":[36],"perform":[37],"a":[38,99,139],"large-scale":[39],"evaluation":[40],"LIT-PCBA":[43],"library":[44],"(15":[45],"targets,":[46],"578,295":[47],"ligand-target":[48],"pairs":[49],"with":[50,67,98,181],"experimentally":[51],"confirmed":[52],"actives":[53],"inactives).":[55],"We":[56,71,165],"compare":[57],"AutoDock-GPU":[58,89],"for":[61,190],"pose":[62],"generation,":[63],"followed":[64],"by":[65],"rescoring":[66,87],"GNINA":[68,86],"NMDN.":[70],"further":[72],"evaluate":[73],"rank-based":[74],"consensus":[75,119],"strategies":[76],"supervised":[78,182],"machine":[79],"learning":[80],"models":[81],"trained":[82],"features.":[85],"of":[88,102,142],"poses":[90],"(AutoDock-GNINA)":[91],"emerged":[92],"strongest":[95],"single":[96,129,169],"method":[97,171],"median":[100,140],"EF1%":[101,141],"2.14.":[103],"DiffDock-based":[104],"approaches":[105],"underperformed":[106],"relative":[107],"to":[108],"AutoDock-GNINA,":[109],"particularly":[110],"challenging":[112],"targets":[113,174],"OPRK1.":[116],"Carefully":[117],"designed":[118],"ranking":[120],"improved":[121],"robustness":[122],"did":[124],"not":[125],"surpass":[126],"best":[128,153],"scorer.":[130],"Supervised":[131],"ML":[132],"re-ranking":[133,183],"delivered":[134],"largest":[136],"gains,":[137],"achieving":[138],"4.49":[143],"(+110%":[144],"over":[145],"AutoDock-GNINA).":[146],"Our":[147],"results":[148],"highlight":[149],"that":[150,167,176],"even":[151],"classical+ML":[154],"hybrid":[155],"workflows":[156],"provide":[157],"only":[158],"modest":[159],"early":[160],"enrichment":[161],"realistic":[163],"benchmarks.":[164],"conclude":[166],"no":[168],"dominates":[172],"across":[173],"rigorously":[177],"validated,":[178],"cost-effective":[179],"combinations":[180],"currently":[184],"offer":[185],"most":[187],"value":[189],"virtual":[191],"screening.":[192]},"counts_by_year":[],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2026-05-06T00:00:00"}