{"id":"https://openalex.org/W4214530112","doi":"https://doi.org/10.1021/acs.jcim.2c00060","title":"Structure-Aware Multimodal Deep Learning for Drug\u2013Protein Interaction Prediction","display_name":"Structure-Aware Multimodal Deep Learning for Drug\u2013Protein Interaction Prediction","publication_year":2022,"publication_date":"2022-02-24","ids":{"openalex":"https://openalex.org/W4214530112","doi":"https://doi.org/10.1021/acs.jcim.2c00060","pmid":"https://pubmed.ncbi.nlm.nih.gov/35200015"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.2c00060","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.2c00060","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/A5013620504","display_name":"Penglei Wang","orcid":"https://orcid.org/0000-0002-1966-3491"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Penglei Wang","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"],"raw_orcid":"https://orcid.org/0000-0002-1966-3491","affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075817762","display_name":"Shuangjia Zheng","orcid":"https://orcid.org/0000-0001-9747-4285"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuangjia Zheng","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen Universit, Guangzhou 510275, China"],"raw_orcid":"https://orcid.org/0000-0001-9747-4285","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen Universit, Guangzhou 510275, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011253369","display_name":"Yize Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yize Jiang","raw_affiliation_strings":["Galixir, Beijing 100080, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Galixir, Beijing 100080, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101703620","display_name":"Chengtao Li","orcid":"https://orcid.org/0000-0003-2346-2753"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengtao Li","raw_affiliation_strings":["Galixir, Beijing 100080, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Galixir, Beijing 100080, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101402442","display_name":"Junhong Liu","orcid":"https://orcid.org/0000-0002-8915-0309"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junhong Liu","raw_affiliation_strings":["Galixir, Beijing 100080, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Galixir, Beijing 100080, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086789945","display_name":"Chang Wen","orcid":"https://orcid.org/0000-0001-7339-3130"},"institutions":[{"id":"https://openalex.org/I4210090512","display_name":"Guangzhou Experimental Station","ror":"https://ror.org/00f2c2516","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210090512","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Wen","raw_affiliation_strings":["Guangzhou Laboratory, Guangzhou 510000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou Laboratory, Guangzhou 510000, China","institution_ids":["https://openalex.org/I4210090512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028024125","display_name":"Atanas Patronov","orcid":"https://orcid.org/0000-0002-9797-6573"},"institutions":[{"id":"https://openalex.org/I4210143795","display_name":"AstraZeneca (Sweden)","ror":"https://ror.org/04wwrrg31","country_code":"SE","type":"company","lineage":["https://openalex.org/I105036370","https://openalex.org/I4210143795"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Atanas Patronov","raw_affiliation_strings":["MolecularAI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 405 30, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MolecularAI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg 405 30, Sweden","institution_ids":["https://openalex.org/I4210143795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081179416","display_name":"Dahong Qian","orcid":"https://orcid.org/0000-0001-9715-6674"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dahong Qian","raw_affiliation_strings":["School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100729956","display_name":"Hongming Chen","orcid":"https://orcid.org/0000-0002-8065-8333"},"institutions":[{"id":"https://openalex.org/I4210090512","display_name":"Guangzhou Experimental Station","ror":"https://ror.org/00f2c2516","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210090512","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongming Chen","raw_affiliation_strings":["Guangzhou Laboratory, Guangzhou 510000, China"],"raw_orcid":"https://orcid.org/0000-0002-8065-8333","affiliations":[{"raw_affiliation_string":"Guangzhou Laboratory, Guangzhou 510000, China","institution_ids":["https://openalex.org/I4210090512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023539493","display_name":"Yuedong Yang","orcid":"https://orcid.org/0000-0002-6782-2813"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuedong Yang","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-Sen Universit, Guangzhou 510275, China"],"raw_orcid":"https://orcid.org/0000-0002-6782-2813","affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-Sen Universit, Guangzhou 510275, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5023539493","https://openalex.org/A5081179416","https://openalex.org/A5100729956"],"corresponding_institution_ids":["https://openalex.org/I157773358","https://openalex.org/I183067930","https://openalex.org/I4210090512"],"apc_list":null,"apc_paid":null,"fwci":11.0118,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.98883758,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"62","issue":"5","first_page":"1308","last_page":"1317"},"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9959999918937683,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9955000281333923,"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/computer-science","display_name":"Computer science","score":0.7144702672958374},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6773247718811035},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5762578845024109},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5687559247016907},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5445889830589294},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5179778337478638},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4986608028411865},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.47465091943740845},{"id":"https://openalex.org/keywords/protein-structure-prediction","display_name":"Protein structure prediction","score":0.4685189425945282},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4661575257778168},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4535466432571411},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.45227816700935364},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.450175017118454},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.4482354521751404},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4247607886791229},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4231581687927246},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4223550260066986},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3963529169559479},{"id":"https://openalex.org/keywords/protein-structure","display_name":"Protein structure","score":0.23332983255386353},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.1341608762741089},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12164512276649475},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10045075416564941}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7144702672958374},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6773247718811035},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5762578845024109},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5687559247016907},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5445889830589294},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5179778337478638},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4986608028411865},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.47465091943740845},{"id":"https://openalex.org/C18051474","wikidata":"https://www.wikidata.org/wiki/Q899656","display_name":"Protein structure prediction","level":3,"score":0.4685189425945282},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4661575257778168},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4535466432571411},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.45227816700935364},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.450175017118454},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.4482354521751404},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4247607886791229},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4231581687927246},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4223550260066986},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3963529169559479},{"id":"https://openalex.org/C47701112","wikidata":"https://www.wikidata.org/wiki/Q735188","display_name":"Protein structure","level":2,"score":0.23332983255386353},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.1341608762741089},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12164512276649475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10045075416564941},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000595","descriptor_name":"Amino Acid Sequence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000595","descriptor_name":"Amino Acid Sequence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000595","descriptor_name":"Amino Acid Sequence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011506","descriptor_name":"Proteins","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D011506","descriptor_name":"Proteins","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D011506","descriptor_name":"Proteins","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","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":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1021/acs.jcim.2c00060","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.2c00060","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:35200015","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35200015","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},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/413886","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/413886","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5600000023841858}],"awards":[{"id":"https://openalex.org/G4783795211","display_name":null,"funder_award_id":"2020YFB0204803","funder_id":"https://openalex.org/F4320321540","funder_display_name":"Ministry of Science and Technology of the People's Republic of China"},{"id":"https://openalex.org/G5108781970","display_name":null,"funder_award_id":"61772566","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321540","display_name":"Ministry of Science and Technology of the People's Republic of China","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1972516851","https://openalex.org/W1981276685","https://openalex.org/W1988037271","https://openalex.org/W2009313526","https://openalex.org/W2092285329","https://openalex.org/W2108069034","https://openalex.org/W2114704115","https://openalex.org/W2134967712","https://openalex.org/W2148145769","https://openalex.org/W2152869198","https://openalex.org/W2156077095","https://openalex.org/W2158714788","https://openalex.org/W2204695023","https://openalex.org/W2557595285","https://openalex.org/W2567812636","https://openalex.org/W2583907533","https://openalex.org/W2607268717","https://openalex.org/W2625283692","https://openalex.org/W2777416523","https://openalex.org/W2781821160","https://openalex.org/W2785947426","https://openalex.org/W2807792492","https://openalex.org/W2808950571","https://openalex.org/W2860192827","https://openalex.org/W2898364362","https://openalex.org/W2918239264","https://openalex.org/W2951433247","https://openalex.org/W2963833291","https://openalex.org/W3005769002","https://openalex.org/W3018980093","https://openalex.org/W3028589594","https://openalex.org/W3030970797","https://openalex.org/W3032123378","https://openalex.org/W3096561213","https://openalex.org/W3129073614","https://openalex.org/W3158144848","https://openalex.org/W6601258443"],"related_works":["https://openalex.org/W4225124612","https://openalex.org/W2043806667","https://openalex.org/W1999699871","https://openalex.org/W2021633306","https://openalex.org/W2006801911","https://openalex.org/W2033669961","https://openalex.org/W1972167985","https://openalex.org/W2971899271","https://openalex.org/W2350644419","https://openalex.org/W2061050749"],"abstract_inverted_index":{"Identifying":[0],"drug-protein":[1],"interactions":[2,166],"(DPIs)":[3],"is":[4,206],"crucial":[5],"in":[6,112,185,198],"drug":[7],"discovery,":[8],"and":[9,59,139,149,155,190,224],"a":[10,83,91,113,124,170],"number":[11],"of":[12,37,146,164],"machine":[13],"learning":[14],"methods":[15,23,45,177],"have":[16],"been":[17],"developed":[18],"to":[19,49],"predict":[20],"DPIs.":[21],"Existing":[22],"usually":[24],"use":[25],"unrealistic":[26],"data":[27,87,94,103,188,201],"sets":[28],"with":[29,106,210],"hidden":[30],"bias,":[31],"which":[32,79,215],"will":[33],"limit":[34],"the":[35,70,131,161,165,186,195,199,211,219],"accuracy":[36],"virtual":[38],"screening":[39],"methods.":[40],"Meanwhile,":[41],"most":[42],"DPI":[43,75,99,175],"prediction":[44,76,176],"pay":[46],"more":[47,114],"attention":[48],"molecular":[50],"representation":[51,58,148,163],"but":[52],"lack":[53],"effective":[54],"research":[55],"on":[56,82],"protein":[57,120,132],"high-level":[60,150],"associations":[61],"between":[62,167,222],"different":[63],"instances.":[64],"To":[65],"this":[66],"end,":[67],"we":[68,122],"present":[69],"novel":[71],"structure-aware":[72,125],"multimodal":[73],"deep":[74],"model,":[77],"STAMP-DPI,":[78],"was":[80],"trained":[81],"curated":[84],"industry-scale":[85,102],"benchmark":[86,93,116],"set.":[88,202],"We":[89],"built":[90],"high-quality":[92],"set":[95,104,189],"named":[96],"GalaxyDB":[97,200],"for":[98,153],"prediction.":[100],"This":[101],"along":[105],"an":[107,207],"unbiased":[108],"training":[109],"procedure":[110],"resulted":[111],"robust":[115],"study.":[117],"For":[118],"informative":[119],"representation,":[121],"constructed":[123],"graph":[126,140],"neural":[127,141],"network":[128],"method":[129],"from":[130],"sequence":[133],"by":[134,178],"combining":[135],"predicted":[136],"contact":[137],"maps":[138],"networks.":[142],"Through":[143],"further":[144],"integration":[145],"structure-based":[147],"pretrained":[151],"embeddings":[152],"molecules":[154,223],"proteins,":[156],"our":[157,204],"model":[158,205,209],"effectively":[159],"captures":[160],"feature":[162],"them.":[168],"As":[169],"result,":[171],"STAMP-DPI":[172],"outperformed":[173],"state-of-the-art":[174],"decreasing":[179],"7.00%":[180],"mean":[181],"square":[182],"error":[183],"(MSE)":[184],"Davis":[187],"improving":[191],"8.89%":[192],"area":[193],"under":[194],"curve":[196],"(AUC)":[197],"Moreover,":[203],"interpretable":[208],"transformer-based":[212],"interaction":[213],"mechanism,":[214],"can":[216],"accurately":[217],"reveal":[218],"binding":[220],"sites":[221],"proteins.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
