{"id":"https://openalex.org/W4313527263","doi":"https://doi.org/10.1109/bibm55620.2022.9995214","title":"Boosting Deep Learning-based Docking with Cross-attention and Centrality Embedding","display_name":"Boosting Deep Learning-based Docking with Cross-attention and Centrality Embedding","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313527263","doi":"https://doi.org/10.1109/bibm55620.2022.9995214"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995214","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995214","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5100691578","display_name":"Jingxuan Wang","orcid":"https://orcid.org/0009-0001-1746-9628"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingxuan Wang","raw_affiliation_strings":["Shandong University,School of Computer Science and Technology,Qingdao,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University,School of Computer Science and Technology,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041699085","display_name":"Zongzhao Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongzhao Qiu","raw_affiliation_strings":["Shandong University,School of Computer Science and Technology,Qingdao,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University,School of Computer Science and Technology,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100342872","display_name":"Xuan Zhang","orcid":"https://orcid.org/0000-0001-5329-7272"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Zhang","raw_affiliation_strings":["Shandong University,School of Computer Science and Technology,Qingdao,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University,School of Computer Science and Technology,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063621983","display_name":"Zhenghe Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenghe Yang","raw_affiliation_strings":["LTHPC (Beijing) Technology Company Limited,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTHPC (Beijing) Technology Company Limited,Beijing,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101671767","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0001-9799-4635"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]},{"id":"https://openalex.org/I4391767886","display_name":"State Key Laboratory of Microbial Technology","ror":"https://ror.org/03x08qn04","country_code":null,"type":"facility","lineage":["https://openalex.org/I154099455","https://openalex.org/I4391767886"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":["Shandong University,State Key Laboratory of Microbial Technology,Qingdao,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University,State Key Laboratory of Microbial Technology,Qingdao,China","institution_ids":["https://openalex.org/I154099455","https://openalex.org/I4391767886"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101906222","display_name":"Xuefeng Cui","orcid":"https://orcid.org/0000-0002-8816-3482"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuefeng Cui","raw_affiliation_strings":["Shandong University,School of Computer Science and Technology,Qingdao,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University,School of Computer Science and Technology,Qingdao,China","institution_ids":["https://openalex.org/I80143920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4976,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83384933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"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":0.9998999834060669,"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":0.9962999820709229,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.9914000034332275,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/docking","display_name":"Docking (animal)","score":0.723029375076294},{"id":"https://openalex.org/keywords/autodock","display_name":"AutoDock","score":0.7005972266197205},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6122474074363708},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5909177660942078},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.556273877620697},{"id":"https://openalex.org/keywords/protein\u2013ligand-docking","display_name":"Protein\u2013ligand docking","score":0.5435445308685303},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5140236616134644},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5029610991477966},{"id":"https://openalex.org/keywords/decoy","display_name":"Decoy","score":0.4868623614311218},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3496769666671753},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.23952266573905945},{"id":"https://openalex.org/keywords/virtual-screening","display_name":"Virtual screening","score":0.17533862590789795},{"id":"https://openalex.org/keywords/molecular-dynamics","display_name":"Molecular dynamics","score":0.16995206475257874},{"id":"https://openalex.org/keywords/computational-chemistry","display_name":"Computational chemistry","score":0.16548112034797668},{"id":"https://openalex.org/keywords/in-silico","display_name":"In silico","score":0.15532630681991577}],"concepts":[{"id":"https://openalex.org/C41685203","wikidata":"https://www.wikidata.org/wiki/Q1974042","display_name":"Docking (animal)","level":2,"score":0.723029375076294},{"id":"https://openalex.org/C2780152424","wikidata":"https://www.wikidata.org/wiki/Q4826062","display_name":"AutoDock","level":4,"score":0.7005972266197205},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122474074363708},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5909177660942078},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.556273877620697},{"id":"https://openalex.org/C77319485","wikidata":"https://www.wikidata.org/wiki/Q7251520","display_name":"Protein\u2013ligand docking","level":4,"score":0.5435445308685303},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5140236616134644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5029610991477966},{"id":"https://openalex.org/C2779179475","wikidata":"https://www.wikidata.org/wiki/Q3545649","display_name":"Decoy","level":3,"score":0.4868623614311218},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3496769666671753},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.23952266573905945},{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.17533862590789795},{"id":"https://openalex.org/C59593255","wikidata":"https://www.wikidata.org/wiki/Q901663","display_name":"Molecular dynamics","level":2,"score":0.16995206475257874},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.16548112034797668},{"id":"https://openalex.org/C2775905019","wikidata":"https://www.wikidata.org/wiki/Q192572","display_name":"In silico","level":3,"score":0.15532630681991577},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C170493617","wikidata":"https://www.wikidata.org/wiki/Q208467","display_name":"Receptor","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995214","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9995214","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1595159159","https://openalex.org/W1813117265","https://openalex.org/W1975447903","https://openalex.org/W1981076832","https://openalex.org/W1986853728","https://openalex.org/W1993046136","https://openalex.org/W2008248968","https://openalex.org/W2039473152","https://openalex.org/W2074978477","https://openalex.org/W2115339329","https://openalex.org/W2128448454","https://openalex.org/W2134967712","https://openalex.org/W2171955717","https://openalex.org/W2578119541","https://openalex.org/W2594153126","https://openalex.org/W2610652256","https://openalex.org/W2805746983","https://openalex.org/W2902812092","https://openalex.org/W2914877211","https://openalex.org/W2964015378","https://openalex.org/W2992752586","https://openalex.org/W3016492430","https://openalex.org/W3185671814","https://openalex.org/W3211394146","https://openalex.org/W3216686093","https://openalex.org/W4206404486","https://openalex.org/W4206488367","https://openalex.org/W4286902310","https://openalex.org/W4293003482","https://openalex.org/W4385245566","https://openalex.org/W6726873649","https://openalex.org/W6739901393","https://openalex.org/W6802810016","https://openalex.org/W6804049574"],"related_works":["https://openalex.org/W2336040088","https://openalex.org/W2136513242","https://openalex.org/W2058674645","https://openalex.org/W2766845742","https://openalex.org/W4385073511","https://openalex.org/W3199423294","https://openalex.org/W1594539819","https://openalex.org/W3113038228","https://openalex.org/W2614191518","https://openalex.org/W2007257620"],"abstract_inverted_index":{"Docking":[0],"is":[1,8,48,56,64,87,108,131],"a":[2,22,65,101,135,141,147],"classic":[3],"computational":[4],"biology":[5],"problem":[6],"that":[7,49,124],"widely":[9],"used":[10,109],"to":[11,16,29,89,110,133],"predict":[12],"binding":[13,19,72,98],"conformations":[14,145,176],"and":[15,54,95,114],"virtually":[17],"screen":[18],"molecules.":[20],"Recently,":[21],"deep":[23,67],"learning-based":[24],"method,":[25],"DeepDock":[26,157],"was":[27],"proposed":[28],"address":[30],"the":[31,46,50,93,96,119,125,166],"docking":[32],"problem.":[33],"The":[34],"method":[35,47],"shows":[36],"great":[37],"performance":[38],"on":[39,105,118],"conformation":[40,73,137,178],"prediction.":[41,74],"One":[42],"major":[43,81],"limitation":[44],"of":[45,52,143,150],"interaction":[51],"ligands":[53],"targets":[55],"too":[57],"simple.":[58],"Here,":[59],"we":[60],"introduce":[61],"caDeepDock,":[62],"which":[63],"geometric":[66],"learning":[68],"model":[69],"for":[70],"protein-ligand":[71],"Inspired":[75],"by":[76,129,158,163,170],"DeepDock,":[77],"caDeepDock":[78,130,171],"has":[79],"two":[80],"advantages":[82],"over":[83],"DeepDock.":[84],"First,":[85],"cross-attention":[86],"employed":[88],"enable":[90],"communications":[91],"between":[92],"molecule":[94],"protein":[97],"pocket.":[99],"Second,":[100],"positional":[102],"embedding":[103],"based":[104],"node":[106],"degrees":[107],"incorporate":[111],"both":[112],"data-dependent":[113],"position-dependent":[115],"communications.":[116],"Experiments":[117],"CASF-2016":[120],"benchmark":[121],"have":[122],"shown":[123],"potential":[126,167],"function":[127,168],"learned":[128,169],"able":[132],"pick":[134],"near-native":[136,175],"(with":[138],"RMSD$\\leqslant$2\u00c5)":[139],"from":[140],"set":[142],"decoy":[144],"with":[146],"success":[148],"rate":[149],"91.6%.":[151],"This":[152],"result":[153],"outperforms":[154],"not":[155],"only":[156],"4.6%":[159],"but":[160],"also":[161],"AutoDock":[162],"1.4%.":[164],"Consequently,":[165],"yields":[172],"5.96%":[173],"more":[174],"in":[177],"optimization":[179],"experiments.":[180]},"counts_by_year":[{"year":2024,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
