{"id":"https://openalex.org/W4297227089","doi":"https://doi.org/10.1021/acs.jcim.2c01057","title":"DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features","display_name":"DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features","publication_year":2022,"publication_date":"2022-09-26","ids":{"openalex":"https://openalex.org/W4297227089","doi":"https://doi.org/10.1021/acs.jcim.2c01057","pmid":"https://pubmed.ncbi.nlm.nih.gov/36154119"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.2c01057","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.2c01057","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015752410","display_name":"Agamemnon Krasoulis","orcid":"https://orcid.org/0000-0002-0468-0627"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Agamemnon Krasoulis","raw_affiliation_strings":["DeepLab, Leoforos Syngrou 106, Athens117 41, Greece"],"affiliations":[{"raw_affiliation_string":"DeepLab, Leoforos Syngrou 106, Athens117 41, Greece","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040958665","display_name":"Nick Antonopoulos","orcid":"https://orcid.org/0000-0002-3175-8338"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nick Antonopoulos","raw_affiliation_strings":["DeepLab, Leoforos Syngrou 106, Athens117 41, Greece"],"affiliations":[{"raw_affiliation_string":"DeepLab, Leoforos Syngrou 106, Athens117 41, Greece","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040922549","display_name":"Vassilis Pitsikalis","orcid":"https://orcid.org/0000-0002-1593-7491"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vassilis Pitsikalis","raw_affiliation_strings":["DeepLab, Leoforos Syngrou 106, Athens117 41, Greece"],"affiliations":[{"raw_affiliation_string":"DeepLab, Leoforos Syngrou 106, Athens117 41, Greece","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034651500","display_name":"Stavros Theodorakis","orcid":"https://orcid.org/0000-0002-8282-3558"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stavros Theodorakis","raw_affiliation_strings":["DeepLab, Leoforos Syngrou 106, Athens117 41, Greece"],"affiliations":[{"raw_affiliation_string":"DeepLab, Leoforos Syngrou 106, Athens117 41, Greece","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5015752410"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7975,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.94208858,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"62","issue":"19","first_page":"4642","last_page":"4659"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"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"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9922999739646912,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/virtual-screening","display_name":"Virtual screening","score":0.7610155940055847},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.719552755355835},{"id":"https://openalex.org/keywords/docking","display_name":"Docking (animal)","score":0.6063279509544373},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5964742302894592},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5890823602676392},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5879521369934082},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5381705164909363},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.4523204565048218},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.167038232088089}],"concepts":[{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.7610155940055847},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.719552755355835},{"id":"https://openalex.org/C41685203","wikidata":"https://www.wikidata.org/wiki/Q1974042","display_name":"Docking (animal)","level":2,"score":0.6063279509544373},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5964742302894592},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5890823602676392},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5879521369934082},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5381705164909363},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.4523204565048218},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.167038232088089},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008024","descriptor_name":"Ligands","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008024","descriptor_name":"Ligands","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008024","descriptor_name":"Ligands","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008565","descriptor_name":"Membrane Proteins","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008565","descriptor_name":"Membrane Proteins","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008565","descriptor_name":"Membrane Proteins","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D011485","descriptor_name":"Protein Binding","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011485","descriptor_name":"Protein Binding","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011485","descriptor_name":"Protein Binding","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057166","descriptor_name":"High-Throughput Screening Assays","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057166","descriptor_name":"High-Throughput Screening Assays","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057166","descriptor_name":"High-Throughput Screening Assays","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D062105","descriptor_name":"Molecular Docking Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D062105","descriptor_name":"Molecular Docking Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D062105","descriptor_name":"Molecular Docking Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1021/acs.jcim.2c01057","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.2c01057","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},{"id":"pmid:36154119","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36154119","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of chemical information and modeling","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1964513093","https://openalex.org/W1968319881","https://openalex.org/W1974809008","https://openalex.org/W1992441011","https://openalex.org/W1993046136","https://openalex.org/W1993285168","https://openalex.org/W1993403967","https://openalex.org/W2000864931","https://openalex.org/W2009423060","https://openalex.org/W2040990879","https://openalex.org/W2042110087","https://openalex.org/W2047509756","https://openalex.org/W2052030759","https://openalex.org/W2052643996","https://openalex.org/W2052666656","https://openalex.org/W2058820833","https://openalex.org/W2074631079","https://openalex.org/W2086286404","https://openalex.org/W2134967712","https://openalex.org/W2148512505","https://openalex.org/W2164088236","https://openalex.org/W2171590421","https://openalex.org/W2176516200","https://openalex.org/W2273267066","https://openalex.org/W2280552799","https://openalex.org/W2558460151","https://openalex.org/W2583907533","https://openalex.org/W2587598315","https://openalex.org/W2600971009","https://openalex.org/W2784213390","https://openalex.org/W2785947426","https://openalex.org/W2803094965","https://openalex.org/W2809216727","https://openalex.org/W2894566366","https://openalex.org/W2895884529","https://openalex.org/W2896002881","https://openalex.org/W2899788782","https://openalex.org/W2902812092","https://openalex.org/W2902820054","https://openalex.org/W2912564562","https://openalex.org/W2918239264","https://openalex.org/W2937307539","https://openalex.org/W2951395093","https://openalex.org/W2969325194","https://openalex.org/W2969457089","https://openalex.org/W2974531988","https://openalex.org/W2978132989","https://openalex.org/W2978484973","https://openalex.org/W2989848927","https://openalex.org/W2992752586","https://openalex.org/W3001433048","https://openalex.org/W3006436762","https://openalex.org/W3007309629","https://openalex.org/W3015572666","https://openalex.org/W3019745511","https://openalex.org/W3023126697","https://openalex.org/W3032123378","https://openalex.org/W3082411326","https://openalex.org/W3096561213","https://openalex.org/W3098189759","https://openalex.org/W3098350627","https://openalex.org/W3104508774","https://openalex.org/W3106162654","https://openalex.org/W3138526880","https://openalex.org/W3168430821","https://openalex.org/W3216686093","https://openalex.org/W4206018883","https://openalex.org/W4206912588","https://openalex.org/W6601955380"],"related_works":["https://openalex.org/W3180887190","https://openalex.org/W2059230675","https://openalex.org/W2376003823","https://openalex.org/W2773493604","https://openalex.org/W2269591907","https://openalex.org/W2027065525","https://openalex.org/W2336040088","https://openalex.org/W2594726310","https://openalex.org/W2355193834","https://openalex.org/W2907597838"],"abstract_inverted_index":{"Computational":[0],"methods":[1],"for":[2,15,58,134],"virtual":[3,92,128,135,249],"screening":[4,93,129,136,179,202,250],"can":[5],"dramatically":[6,206],"accelerate":[7],"early-stage":[8],"drug":[9],"discovery":[10],"by":[11,29],"identifying":[12],"potential":[13,57,255],"hits":[14],"a":[16,35,40,86,120,220],"specified":[17],"target.":[18],"Docking":[19],"algorithms":[20,66,98],"traditionally":[21],"use":[22,228],"physics-based":[23],"simulations":[24],"to":[25,85,156,192,247,256,258],"address":[26],"this":[27,109],"challenge":[28],"estimating":[30],"the":[31,46,70,73,90,103,113,168,227,254],"binding":[32,42,76],"orientation":[33],"of":[34,72,89,97,115,176,212,222,229,260],"query":[36],"protein-ligand":[37,74],"pair":[38],"and":[39,50,161,187,224,232],"corresponding":[41],"affinity":[43],"score.":[44],"Over":[45],"recent":[47],"years,":[48],"classical":[49],"modern":[51],"machine":[52,159,197],"learning":[53,198],"architectures":[54],"have":[55],"shown":[56],"outperforming":[59],"traditional":[60],"docking":[61,81,170],"algorithms.":[62,165],"Nevertheless,":[63],"most":[64],"learning-based":[65,164],"still":[67],"rely":[68],"on":[69,145],"availability":[71],"complex":[75],"pose,":[77],"typically":[78],"estimated":[79],"via":[80,235],"simulations,":[82],"which":[83],"leads":[84],"severe":[87],"slowdown":[88],"overall":[91],"process.":[94],"A":[95],"family":[96],"processing":[99,116],"target":[100],"information":[101],"at":[102,112,119],"amino":[104,194],"acid":[105,195],"sequence":[106],"level":[107],"avoid":[108],"requirement,":[110],"however,":[111],"cost":[114],"protein":[117,216],"data":[118,233],"higher":[121,182],"representation":[122],"level.":[123],"We":[124],"introduce":[125],"deep":[126],"neural":[127,139],"(DENVIS),":[130],"an":[131,193],"end-to-end":[132],"pipeline":[133],"using":[137,219,262],"graph":[138],"networks":[140],"(GNNs).":[141],"By":[142,166],"performing":[143],"experiments":[144],"two":[146],"benchmark":[147],"databases,":[148],"we":[149],"show":[150],"that":[151],"our":[152,213],"method":[153],"performs":[154],"competitively":[155],"several":[157,174],"docking-based,":[158],"learning-based,":[160],"hybrid":[162,188],"docking/machine":[163],"avoiding":[167],"intermediate":[169],"step,":[171],"DENVIS":[172,204,244],"exhibits":[173],"orders":[175],"magnitude":[177],"faster":[178],"times":[180],"(i.e.,":[181],"throughput)":[183],"than":[184],"both":[185],"docking-based":[186],"models.":[189],"When":[190],"compared":[191],"sequence-based":[196],"model":[199,230,240],"with":[200],"comparable":[201],"times,":[203],"achieves":[205,245],"better":[207],"performance.":[208],"Some":[209],"key":[210],"elements":[211],"approach":[214],"include":[215],"pocket":[217],"modeling":[218],"combination":[221],"atomic":[223],"surface":[225],"features,":[226],"ensembles,":[231],"augmentation":[234],"artificial":[236],"negative":[237],"sampling":[238],"during":[239],"training.":[241],"In":[242],"summary,":[243],"competitive":[246],"state-of-the-art":[248],"performance,":[251],"while":[252],"offering":[253],"scale":[257],"billions":[259],"molecules":[261],"minimal":[263],"computational":[264],"resources.":[265]},"counts_by_year":[{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
