{"id":"https://openalex.org/W4416249830","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228548","title":"GGANet: A Geometry-Enhanced Gated Attention Network for Drug-Target Interaction Prediction","display_name":"GGANet: A Geometry-Enhanced Gated Attention Network for Drug-Target Interaction Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249830","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228548"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5043250500","display_name":"Hong Xia Luo","orcid":"https://orcid.org/0000-0001-7021-3923"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hong Luo","raw_affiliation_strings":["Chengdu University of Information Technology,School of Computer Science,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology,School of Computer Science,Chengdu,China","institution_ids":["https://openalex.org/I24201400"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100741917","display_name":"Yongqing Zhang","orcid":"https://orcid.org/0000-0003-3422-8305"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongqing Zhang","raw_affiliation_strings":["Chengdu University of Information Technology,School of Computer Science,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology,School of Computer Science,Chengdu,China","institution_ids":["https://openalex.org/I24201400"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043250500"],"corresponding_institution_ids":["https://openalex.org/I24201400"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3674385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9560999870300293,"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":0.9560999870300293,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.01640000008046627,"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":0.0052999998442828655,"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/robustness","display_name":"Robustness (evolution)","score":0.7717999815940857},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5655999779701233},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5533999800682068},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5389999747276306},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46160000562667847},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.3813000023365021},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3684000074863434},{"id":"https://openalex.org/keywords/interaction-information","display_name":"Interaction information","score":0.3571000099182129}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7717999815940857},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7184000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.680400013923645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5655999779701233},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5533999800682068},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5389999747276306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46160000562667847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C38764148","wikidata":"https://www.wikidata.org/wiki/Q17098245","display_name":"Interaction information","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.32910001277923584},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30090001225471497},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228548","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2010004274","https://openalex.org/W2100257305","https://openalex.org/W2108069034","https://openalex.org/W2153838454","https://openalex.org/W2165674132","https://openalex.org/W2170146596","https://openalex.org/W2780845733","https://openalex.org/W2860192827","https://openalex.org/W2899788782","https://openalex.org/W3000043291","https://openalex.org/W3005769002","https://openalex.org/W3006737593","https://openalex.org/W3018980093","https://openalex.org/W3028589594","https://openalex.org/W3096561213","https://openalex.org/W3136918052","https://openalex.org/W3157265962","https://openalex.org/W3177500196","https://openalex.org/W3206585172","https://openalex.org/W3215525389","https://openalex.org/W4205773061","https://openalex.org/W4213077304","https://openalex.org/W4290993847","https://openalex.org/W4307178567","https://openalex.org/W4323653718","https://openalex.org/W4327550249","https://openalex.org/W4385245566","https://openalex.org/W4388409550","https://openalex.org/W4388464011","https://openalex.org/W4392693790"],"related_works":[],"abstract_inverted_index":{"Drug-target":[0],"interaction":[1],"(DTI)":[2],"prediction":[3],"is":[4],"a":[5,84],"critical":[6],"step":[7],"in":[8,14,134],"drug":[9,65],"discovery.":[10],"Despite":[11],"significant":[12],"advances":[13],"deep":[15],"learning-based":[16],"methods,":[17,132],"data":[18],"representation":[19],"and":[20,33,45,64,105,124,141],"feature":[21,107,116],"alignment":[22],"challenges":[23],"remain.":[24],"Specifically,":[25],"previous":[26],"methods":[27],"often":[28],"rely":[29],"on":[30,121],"low-dimensional":[31,95],"representations":[32,108],"limited":[34],"labeled":[35],"data,":[36,47,137],"overlooking":[37],"the":[38,50,76,79,125],"importance":[39],"of":[40,52,78,146],"high-dimensional":[41,99],"spatial":[42],"geometric":[43,100],"information":[44,101],"unlabeled":[46],"which":[48,74],"restricts":[49],"extraction":[51],"crucial":[53],"features.":[54],"Additionally,":[55],"most":[56],"approaches":[57],"align":[58],"features":[59],"between":[60],"amino":[61],"acid":[62],"residues":[63],"atoms":[66],"using":[67],"dot-product":[68],"similarity,":[69],"ignoring":[70],"their":[71],"biochemical":[72],"differences,":[73],"limits":[75],"effectiveness":[77],"alignment.":[80,117],"We":[81,118],"propose":[82],"GGANet,":[83],"geometry-enhanced":[85],"gated":[86,111],"attention":[87,112],"network,":[88],"to":[89,102,113],"address":[90],"these":[91],"limitations.":[92],"GGANet":[93,129,147],"integrates":[94],"pre-trained":[96],"embeddings":[97],"with":[98],"generate":[103],"robust":[104],"generalizable":[106],"while":[109],"employing":[110],"ensure":[114],"efficient":[115],"conducted":[119],"experiments":[120],"four":[122],"datasets,":[123],"results":[126],"show":[127],"that":[128],"outperforms":[130],"baseline":[131],"especially":[133],"predicting":[135],"unseen":[136],"demonstrating":[138],"superior":[139],"robustness":[140],"generalization.":[142],"The":[143],"implementation":[144],"details":[145],"are":[148],"available":[149],"at":[150],"https://github.com/ZhangLab312/GGANet.":[151]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
