{"id":"https://openalex.org/W4414116796","doi":"https://doi.org/10.1109/tetci.2025.3604800","title":"TFT-CPI: A Novel Feature-Enhanced Model for Compound-Protein Interaction Prediction Based on Topology and Fractal Theory","display_name":"TFT-CPI: A Novel Feature-Enhanced Model for Compound-Protein Interaction Prediction Based on Topology and Fractal Theory","publication_year":2025,"publication_date":"2025-09-11","ids":{"openalex":"https://openalex.org/W4414116796","doi":"https://doi.org/10.1109/tetci.2025.3604800"},"language":"en","primary_location":{"id":"doi:10.1109/tetci.2025.3604800","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2025.3604800","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-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/A5026561219","display_name":"Yaguo Dong","orcid":"https://orcid.org/0009-0005-1632-1401"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaguo Dong","raw_affiliation_strings":["National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0009-0005-1632-1401","affiliations":[{"raw_affiliation_string":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101908375","display_name":"Meiling Xu","orcid":"https://orcid.org/0000-0002-4226-5651"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meiling Xu","raw_affiliation_strings":["Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-4226-5651","affiliations":[{"raw_affiliation_string":"Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Shenyang, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080304378","display_name":"Lixin Tang","orcid":"https://orcid.org/0000-0002-9950-5169"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixin Tang","raw_affiliation_strings":["National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-9950-5169","affiliations":[{"raw_affiliation_string":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23752362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"1","first_page":"815","last_page":"829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9936000108718872,"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":0.9936000108718872,"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":0.9731000065803528,"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/T13937","display_name":"Genetics, Bioinformatics, and Biomedical Research","score":0.9078999757766724,"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/graph","display_name":"Graph","score":0.5778999924659729},{"id":"https://openalex.org/keywords/fractal","display_name":"Fractal","score":0.5414999723434448},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5239999890327454},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4934000074863434},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.45890000462532043},{"id":"https://openalex.org/keywords/fractal-dimension","display_name":"Fractal dimension","score":0.4551999866962433},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.4401000142097473},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.43070000410079956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6144000291824341},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5778999924659729},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.5414999723434448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45890000462532043},{"id":"https://openalex.org/C26546657","wikidata":"https://www.wikidata.org/wiki/Q1412452","display_name":"Fractal dimension","level":3,"score":0.4551999866962433},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.43070000410079956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41519999504089355},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39250001311302185},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33390000462532043},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.328900009393692},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C162494671","wikidata":"https://www.wikidata.org/wiki/Q2845227","display_name":"Fractal analysis","level":4,"score":0.27790001034736633},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetci.2025.3604800","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2025.3604800","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2311391187","display_name":null,"funder_award_id":"B16009","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G3162652209","display_name":null,"funder_award_id":"62176049","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/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1994233698","https://openalex.org/W2007221293","https://openalex.org/W2009313526","https://openalex.org/W2093221728","https://openalex.org/W2108069034","https://openalex.org/W2148145769","https://openalex.org/W2592742128","https://openalex.org/W2809216727","https://openalex.org/W2860192827","https://openalex.org/W2899887039","https://openalex.org/W2916556269","https://openalex.org/W2955888840","https://openalex.org/W2981094887","https://openalex.org/W2996544626","https://openalex.org/W2999177618","https://openalex.org/W3004946973","https://openalex.org/W3028589594","https://openalex.org/W3032400974","https://openalex.org/W3080409317","https://openalex.org/W3088939268","https://openalex.org/W3093397714","https://openalex.org/W3096561213","https://openalex.org/W4205167309","https://openalex.org/W4225401158","https://openalex.org/W4285157402","https://openalex.org/W4285188807","https://openalex.org/W4313172443","https://openalex.org/W4317702934","https://openalex.org/W4318981550","https://openalex.org/W4324116430","https://openalex.org/W4385756443","https://openalex.org/W4385950555","https://openalex.org/W4386976834","https://openalex.org/W4387212992","https://openalex.org/W4387673203","https://openalex.org/W4389616450","https://openalex.org/W4390706297","https://openalex.org/W4392151641","https://openalex.org/W4392589742","https://openalex.org/W4393405247","https://openalex.org/W4400142953","https://openalex.org/W4401141539","https://openalex.org/W4401142008","https://openalex.org/W4402350548","https://openalex.org/W4404914448","https://openalex.org/W4407213915","https://openalex.org/W4408346268","https://openalex.org/W4410949639"],"related_works":["https://openalex.org/W2013332237","https://openalex.org/W1522259561","https://openalex.org/W4313556169","https://openalex.org/W2295517574","https://openalex.org/W2380221404","https://openalex.org/W2373248682","https://openalex.org/W1968456887","https://openalex.org/W2225738837","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,66,133,229],"of":[2,38,43,170],"compound-protein":[3],"interaction":[4],"(CPI)":[5],"plays":[6],"an":[7,166],"important":[8],"role":[9],"in":[10,27,33,49,217],"drug":[11],"discovery":[12],"and":[13,71,84,108,147,174,182,187,198,207,219],"bioinformatics.":[14],"While":[15],"graph":[16,25,97],"neural":[17,111],"networks":[18,99,112],"have":[19],"attracted":[20],"increasing":[21],"interest":[22],"for":[23,119],"molecular":[24,80,106],"representation":[26],"CPI":[28,51,65,228],"prediction,":[29],"they":[30],"perform":[31],"poorly":[32],"capturing":[34],"global":[35,81],"structural":[36,82],"features":[37,83,107],"compounds.":[39],"Moreover,":[40],"the":[41,59,137,145,179,184,194],"integration":[42],"protein":[44,115,120],"physicochemical":[45],"properties":[46],"remains":[47],"limited":[48],"current":[50],"models.":[52],"To":[53],"address":[54],"these":[55],"limitations,":[56],"we":[57,95],"propose":[58],"TFT-CPI":[60,163,212],"model,":[61],"a":[62,127,158],"novel":[63],"feature-enhanced":[64],"model":[67,124,164,213],"based":[68],"on":[69,139,172,189],"topology":[70],"fractal":[72,86,117],"theory.":[73],"Topological":[74],"data":[75],"analysis":[76],"can":[77],"effectively":[78],"capture":[79],"sequence":[85,116],"dimension":[87,118],"is":[88],"used":[89],"to":[90,104,131,178,193],"extract":[91,105],"hydrophilicity":[92],"information.":[93],"Specifically,":[94],"use":[96,109],"convolutional":[98,110],"combined":[100,113],"with":[101,114,150],"persistence":[102],"landscapes":[103],"feature":[121],"extraction.":[122],"The":[123],"also":[125],"incorporates":[126],"symmetric":[128],"Transformer":[129],"architecture":[130],"improve":[132],"performance.":[134],"We":[135],"conducted":[136],"experiments":[138],"seven":[140],"different":[141],"benchmark":[142],"datasets,":[143],"including":[144],"Human":[146,173],"C.elegans":[148,175],"datasets":[149,176],"varying":[151],"positive-to-negative":[152],"sample":[153],"ratios,":[154],"as":[155,157],"well":[156],"label":[159],"reversal":[160],"dataset,":[161],"GPCR.":[162],"achieved":[165],"average":[167],"AUC":[168,186],"improvement":[169],"0.4%":[171],"compared":[177,192],"state-of-the-art":[180],"models":[181],"attained":[183],"highest":[185],"PRC":[188],"GPCR":[190],"dataset":[191],"baselines,":[195],"reaching":[196],"86.4%":[197],"87.6%,":[199],"respectively.":[200],"These":[201],"results":[202],"highlight":[203],"its":[204,224],"superior":[205],"performance":[206,216],"strong":[208],"generalization":[209],"capabilities.":[210],"Furthermore,":[211],"exhibits":[214],"promising":[215],"out-of-distribution":[218],"cross-dataset":[220],"scenarios,":[221],"further":[222],"demonstrating":[223],"effectiveness":[225],"across":[226],"diverse":[227],"tasks.":[230]},"counts_by_year":[],"updated_date":"2026-01-26T23:06:41.788003","created_date":"2025-10-10T00:00:00"}
