{"id":"https://openalex.org/W4394962334","doi":"https://doi.org/10.1021/acs.jcim.4c00046","title":"Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue","display_name":"Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue","publication_year":2024,"publication_date":"2024-04-19","ids":{"openalex":"https://openalex.org/W4394962334","doi":"https://doi.org/10.1021/acs.jcim.4c00046","pmid":"https://pubmed.ncbi.nlm.nih.gov/38639496"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.4c00046","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.4c00046","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/A5045140841","display_name":"Koichi Handa","orcid":"https://orcid.org/0000-0003-2748-9742"},"institutions":[{"id":"https://openalex.org/I90469020","display_name":"Teijin (Japan)","ror":"https://ror.org/038kxkq33","country_code":"JP","type":"company","lineage":["https://openalex.org/I90469020"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Koichi Handa","raw_affiliation_strings":["Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"],"raw_orcid":"https://orcid.org/0000-0003-2748-9742","affiliations":[{"raw_affiliation_string":"Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan","institution_ids":["https://openalex.org/I90469020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102408503","display_name":"Saki Yoshimura","orcid":null},"institutions":[{"id":"https://openalex.org/I90469020","display_name":"Teijin (Japan)","ror":"https://ror.org/038kxkq33","country_code":"JP","type":"company","lineage":["https://openalex.org/I90469020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Saki Yoshimura","raw_affiliation_strings":["Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan","institution_ids":["https://openalex.org/I90469020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027609089","display_name":"Michiharu Kageyama","orcid":"https://orcid.org/0000-0001-7449-593X"},"institutions":[{"id":"https://openalex.org/I90469020","display_name":"Teijin (Japan)","ror":"https://ror.org/038kxkq33","country_code":"JP","type":"company","lineage":["https://openalex.org/I90469020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Michiharu Kageyama","raw_affiliation_strings":["Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan","institution_ids":["https://openalex.org/I90469020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109282158","display_name":"Takeshi Iijima","orcid":"https://orcid.org/0009-0000-0305-9401"},"institutions":[{"id":"https://openalex.org/I90469020","display_name":"Teijin (Japan)","ror":"https://ror.org/038kxkq33","country_code":"JP","type":"company","lineage":["https://openalex.org/I90469020"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Iijima","raw_affiliation_strings":["Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan","institution_ids":["https://openalex.org/I90469020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5045140841"],"corresponding_institution_ids":["https://openalex.org/I90469020"],"apc_list":null,"apc_paid":null,"fwci":1.9031,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.86559375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"64","issue":"9","first_page":"3662","last_page":"3669"},"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/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9925000071525574,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9807999730110168,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantitative-structure\u2013activity-relationship","display_name":"Quantitative structure\u2013activity relationship","score":0.7722216248512268},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.45222926139831543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4481123387813568},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4478592872619629},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.44051438570022583},{"id":"https://openalex.org/keywords/biological-system","display_name":"Biological system","score":0.4053880572319031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.387816458940506},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36748528480529785},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32525739073753357},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32048141956329346},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.1419984996318817}],"concepts":[{"id":"https://openalex.org/C164126121","wikidata":"https://www.wikidata.org/wiki/Q766383","display_name":"Quantitative structure\u2013activity relationship","level":2,"score":0.7722216248512268},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45222926139831543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4481123387813568},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4478592872619629},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.44051438570022583},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.4053880572319031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.387816458940506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36748528480529785},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32525739073753357},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32048141956329346},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.1419984996318817},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D004364","descriptor_name":"Pharmaceutical Preparations","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008954","descriptor_name":"Models, Biological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008954","descriptor_name":"Models, Biological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008954","descriptor_name":"Models, Biological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008954","descriptor_name":"Models, Biological","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014018","descriptor_name":"Tissue Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014018","descriptor_name":"Tissue Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014018","descriptor_name":"Tissue Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014018","descriptor_name":"Tissue Distribution","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021281","descriptor_name":"Quantitative Structure-Activity Relationship","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D021281","descriptor_name":"Quantitative Structure-Activity Relationship","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D021281","descriptor_name":"Quantitative Structure-Activity Relationship","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D021281","descriptor_name":"Quantitative Structure-Activity Relationship","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1021/acs.jcim.4c00046","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.4c00046","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:38639496","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38639496","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":[{"score":0.6200000047683716,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W938245772","https://openalex.org/W1820155366","https://openalex.org/W1929386789","https://openalex.org/W1967910633","https://openalex.org/W1982652088","https://openalex.org/W1983118095","https://openalex.org/W2007465986","https://openalex.org/W2026015795","https://openalex.org/W2027143209","https://openalex.org/W2032371602","https://openalex.org/W2033158777","https://openalex.org/W2058220696","https://openalex.org/W2070719925","https://openalex.org/W2074772662","https://openalex.org/W2113830209","https://openalex.org/W2128655470","https://openalex.org/W2210241103","https://openalex.org/W2783299760","https://openalex.org/W2801991413","https://openalex.org/W2899385878","https://openalex.org/W2903431239","https://openalex.org/W2966357564","https://openalex.org/W2972499938","https://openalex.org/W2982515304","https://openalex.org/W3024330278","https://openalex.org/W3119101435","https://openalex.org/W3164025371","https://openalex.org/W4292616203","https://openalex.org/W4319656925","https://openalex.org/W4379094088"],"related_works":["https://openalex.org/W2382340815","https://openalex.org/W1972650408","https://openalex.org/W2551736466","https://openalex.org/W1997566113","https://openalex.org/W3189652131","https://openalex.org/W2908740012","https://openalex.org/W1967793880","https://openalex.org/W2792066564","https://openalex.org/W1186905254","https://openalex.org/W2898703579"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"is":[2,21,80,98],"expected":[3],"to":[4,25,36,45,185,319,351,363],"help":[5],"identify":[6],"excellent":[7],"candidates":[8],"in":[9,88,118,143,166,191,205,294,366,377],"drug":[10,86],"discovery.":[11],"However,":[12],"we":[13,34,71,110,149,196,231,328,345],"face":[14],"a":[15,38,47,68,99,160,177,181,192,198,324,330,347],"lack":[16],"of":[17,63,101,115,133,213,236,258,298,340,369],"data,":[18],"as":[19,227,252,282],"it":[20],"time-consuming":[22],"and":[23,90,97,103,129,174,239,267,286,304],"expensive":[24],"acquire":[26],"raw":[27],"data":[28,54,58,105,157,194,250],"perfectly":[29],"for":[30,84,145,323],"many":[31],"compounds.":[32],"Hence,":[33],"tried":[35],"develop":[37],"novel":[39,331,348],"quantitative":[40],"structure-activity":[41],"relationship":[42,332],"(QSAR)":[43],"method":[44,359],"predict":[46,186,320,352],"parameter":[48,83],"more":[49],"precisely":[50],"from":[51,247],"an":[52,81],"incomplete":[53],"set":[55,158,251],"via":[56],"optimizing":[57],"handling":[59],"by":[60,154,180,222,336],"making":[61],"use":[62],"predicted":[64,111,150,221],"explanatory":[65,228,237,341],"variables.":[66,229,342],"As":[67],"case":[69],"study":[70],"focused":[72],"on":[73,263,313],"the":[74,92,112,140,155,187,210,223,241,244,248,253,264,273,283,295,310,321,367,378],"tissue-to-plasma":[75],"partition":[76],"coefficient":[77],"(Kp),":[78],"which":[79,372],"important":[82],"understanding":[85],"distribution":[87],"tissues":[89,120,215,315],"building":[91],"physiologically":[93],"based":[94,262],"pharmacokinetic":[95],"model":[96,164,201,226,242,350],"representative":[100],"small":[102],"sparse":[104],"sets.":[106],"In":[107,343],"this":[108,358],"study,":[109],"Kp":[113,144,152,189,211,259,296,311,353],"values":[114,142,153,190,212,297,376],"119":[116],"compounds":[117],"nine":[119],"(adipose,":[121],"brain,":[122,301],"gut,":[123],"heart,":[124],"kidney,":[125,302],"liver,":[126,303],"lung,":[127],"muscle,":[128],"skin),":[130],"although":[131],"some":[132],"these":[134],"were":[135,292],"not":[136,203],"available.":[137],"To":[138],"fill":[139],"missing":[141,375],"each":[146,334],"tissue,":[147,300],"first":[148,224],"those":[151],"nonmissing":[156],"using":[159],"random":[161],"forest":[162],"(RF)":[163],"with":[165,202,243],"vitro":[167,206],"parameters":[168,207],"(log":[169],"P,":[170],"fu,":[171],"Drug":[172],"Class,":[173],"fi)":[175],"like":[176],"classical":[178],"prediction":[179,260],"QSAR":[182],"model.":[183,255],"Next,":[184],"tissue-specific":[188],"test":[193,249],"set,":[195],"constructed":[197],"second":[199],"RF":[200,225,285,349],"only":[204],"but":[208],"also":[209],"other":[214,217,277,314],"(i.e.,":[216],"than":[218],"target":[219],"tissues)":[220],"Furthermore,":[230],"tested":[232],"all":[233,338],"possible":[234],"combinations":[235,339],"variables":[238],"selected":[240],"highest":[245],"predictability":[246],"final":[254],"The":[256],"evaluation":[257],"accuracy":[261],"root-mean-square":[265],"error":[266],"R":[268],"2":[269],"value":[270],"revealed":[271],"that":[272,309,357],"proposed":[274],"models":[275],"outperformed":[276],"machine":[278],"learning":[279],"methods":[280],"such":[281],"conventional":[284],"message-passing":[287],"neural":[288],"networks.":[289],"Significant":[290],"improvements":[291,307],"observed":[293],"adipose":[299],"skin.":[305],"These":[306],"indicated":[308],"information":[312],"can":[316],"be":[317,361],"used":[318],"same":[322],"specific":[325],"tissue.":[326],"Additionally,":[327],"found":[329],"between":[333],"tissue":[335],"evaluating":[337],"conclusion,":[344],"developed":[346],"values.":[354],"We":[355],"hope":[356],"will":[360],"applied":[362],"various":[364],"problems":[365],"field":[368],"experimental":[370],"biology":[371],"often":[373],"contains":[374],"near":[379],"future.":[380]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4}],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-10-10T00:00:00"}
