{"id":"https://openalex.org/W3017574835","doi":"https://doi.org/10.36334/modsim.2019.a1.hudson","title":"Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)","display_name":"Artificial Neural Networks & Random Forest Classification of druggable molecules and disease targets via scoring functions (SFs)","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3017574835","doi":"https://doi.org/10.36334/modsim.2019.a1.hudson","mag":"3017574835"},"language":"en","primary_location":{"id":"doi:10.36334/modsim.2019.a1.hudson","doi":"https://doi.org/10.36334/modsim.2019.a1.hudson","is_oa":true,"landing_page_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","pdf_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","source":{"id":"https://openalex.org/S4386872464","display_name":"MODSIM","issn_l":"2205-5061","issn":["2205-5061"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"El Sawah, S. (ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040675759","display_name":"Irene L. Hudson","orcid":"https://orcid.org/0000-0002-9000-9556","observed_orcids":["https://orcid.org/0000-0002-9000-9556"]},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Hudson,\u00a0Irene","raw_affiliation_strings":["Department of Mathematical Sciences, College of Science, Engineering and Health, Royal Melbourne Institute of Technology (RMIT), Melbourne, Victoria, Australia,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, College of Science, Engineering and Health, Royal Melbourne Institute of Technology (RMIT), Melbourne, Victoria, Australia,","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067238438","display_name":"Shalem Leemaqz","orcid":"https://orcid.org/0000-0003-4616-8426","observed_orcids":["https://orcid.org/0000-0003-4616-8426"]},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"The University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I4405259444","https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Leemaqz,\u00a0S","raw_affiliation_strings":["Robinson Research Institute, Adelaide Medical School, University of Adelaide, South Australia,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robinson Research Institute, Adelaide Medical School, University of Adelaide, South Australia,","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033537928","display_name":"Andrew David Abell","orcid":"https://orcid.org/0000-0002-0604-2629","observed_orcids":["https://orcid.org/0000-0002-0604-2629"]},"institutions":[{"id":"https://openalex.org/I4391768102","display_name":"ARC Centre for Nanoscale BioPhotonics","ror":"https://ror.org/00g76w893","country_code":"AU","type":"facility","lineage":["https://openalex.org/I4391768102"]},{"id":"https://openalex.org/I5681781","display_name":"The University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I4405259444","https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Abell,\u00a0A","raw_affiliation_strings":["Department of Chemistry, Adelaide Node Director Centre for Nanoscale BioPhotonics (CNBP),University of Adelaide, Adelaide, South Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Chemistry, Adelaide Node Director Centre for Nanoscale BioPhotonics (CNBP),University of Adelaide, Adelaide, South Australia","institution_ids":["https://openalex.org/I4391768102","https://openalex.org/I5681781"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5040675759"],"corresponding_institution_ids":["https://openalex.org/I82951845"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23149458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":1.0,"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":1.0,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.0,"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"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.0,"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/molecular-parameters","display_name":"molecular parameters","score":0.6899999976158142},{"id":"https://openalex.org/keywords/scoring-functions","display_name":"scoring functions","score":0.7250000238418579},{"id":"https://openalex.org/keywords/lipinski-s-rule-of-five","display_name":"Lipinski's rule of five","score":0.9110000133514404},{"id":"https://openalex.org/keywords/logd","display_name":"logD","score":0.5230000019073486},{"id":"https://openalex.org/keywords/octanol-water-partition-coefficient","display_name":"octanol-water partition coefficient","score":0.9049999713897705},{"id":"https://openalex.org/keywords/polar-surface-area","display_name":"polar surface area","score":0.8669999837875366},{"id":"https://openalex.org/keywords/rotatable-bonds","display_name":"rotatable bonds","score":0.8579999804496765},{"id":"https://openalex.org/keywords/molecular-weight","display_name":"molecular weight","score":0.9070000052452087},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.5090000033378601},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9860000014305115},{"id":"https://openalex.org/keywords/target-prioritization","display_name":"target prioritization","score":0.546999990940094},{"id":"https://openalex.org/keywords/molecular-descriptors","display_name":"molecular descriptors","score":0.42500001192092896},{"id":"https://openalex.org/keywords/drug-likeness-prediction","display_name":"drug-likeness prediction","score":0.5809999704360962},{"id":"https://openalex.org/keywords/partition-coefficient","display_name":"partition coefficient","score":0.38600000739097595},{"id":"https://openalex.org/keywords/lipophilicity","display_name":"lipophilicity","score":0.6790000200271606},{"id":"https://openalex.org/keywords/recursive-partitioning","display_name":"recursive partitioning","score":0.4480000138282776}],"concepts":[{"id":"https://openalex.org/C10679952","wikidata":"https://www.wikidata.org/wiki/Q5308937","display_name":"Druggability","level":3,"score":0.7997763156890869},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7756959199905396},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6565775871276855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6452692151069641},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5485027432441711},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4231581687927246},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.12994754314422607},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.06864944100379944},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.36334/modsim.2019.a1.hudson","doi":"https://doi.org/10.36334/modsim.2019.a1.hudson","is_oa":true,"landing_page_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","pdf_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","source":{"id":"https://openalex.org/S4386872464","display_name":"MODSIM","issn_l":"2205-5061","issn":["2205-5061"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"El Sawah, S. (ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.","raw_type":"proceedings-article"},{"id":"pmh:oai:alma61RMIT.INST:11247354720001341","doi":null,"is_oa":true,"landing_page_url":"https://doi.org/10.36334/modsim.2019.A1.hudson","pdf_url":null,"source":{"id":"https://openalex.org/S4306402074","display_name":"RMIT Research Repository (RMIT University Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I82951845","host_organization_name":"RMIT University","host_organization_lineage":["https://openalex.org/I82951845"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"}],"best_oa_location":{"id":"doi:10.36334/modsim.2019.a1.hudson","doi":"https://doi.org/10.36334/modsim.2019.a1.hudson","is_oa":true,"landing_page_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","pdf_url":"https://doi.org/10.36334/modsim.2019.a1.hudson","source":{"id":"https://openalex.org/S4386872464","display_name":"MODSIM","issn_l":"2205-5061","issn":["2205-5061"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"El Sawah, S. (ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8048999905586243,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3017574835.pdf","grobid_xml":"https://content.openalex.org/works/W3017574835.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W2018518196","https://openalex.org/W2053717624","https://openalex.org/W2234248612","https://openalex.org/W2237111426","https://openalex.org/W2321029289","https://openalex.org/W2345340078","https://openalex.org/W2461440102","https://openalex.org/W2539985226","https://openalex.org/W2554124061","https://openalex.org/W2591766662","https://openalex.org/W2783666262","https://openalex.org/W2902225381","https://openalex.org/W2904290809","https://openalex.org/W2998342339","https://openalex.org/W4255252731","https://openalex.org/W6631263138"],"related_works":["https://openalex.org/W3042501251","https://openalex.org/W2904290809","https://openalex.org/W2756457116","https://openalex.org/W2998342339","https://openalex.org/W4384820162","https://openalex.org/W3198847015","https://openalex.org/W3011200991","https://openalex.org/W6920685145","https://openalex.org/W4403211351","https://openalex.org/W7159966854"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"machine":[3],"learning":[4],"has":[5,17,540,568],"played":[6],"an":[7],"increasing":[8],"role":[9],"to":[10,45,50,76,81,93,129,227,235,295,349,377,516,521,546,574,606,622,630,679,689],"help":[11],"identify":[12,82,680],"druggable":[13,83,681,709],"molecules.In":[14],"particular":[15],"research":[16],"shown":[18,688],"that":[19],"random":[20],"forests":[21],"(RFs),":[22],"recursive":[23],"partitioning":[24],"(RP),":[25],"support":[26],"vector":[27],"machines":[28],"(SVMs)":[29],"and":[30,58,65,91,101,108,117,145,168,171,174,187,212,246,267,292,390,417,536,618,627,660,676,683,687,711],"artificial":[31],"neural":[32],"networks":[33],"(ANNs)":[34,116],"have":[35],"been":[36],"commonly":[37],"employed":[38],"in":[39,298,548,559,587,645],"this":[40,69],"arena.Expanding":[41],"disease":[42,53,84,109,131],"modifying":[43],"targets":[44,54,85,132,684],"pharmacological":[46],"manipulation":[47],"is":[48,72,375,526,581],"vital":[49],"human":[51],"health.Modelling":[52],"allow":[55],"for":[56,180,239,301,336,472,531,665],"prediction":[57],"prioritisation":[59],"based":[60,121,436],"on":[61,122],"their":[62],"molecular":[63,88,107,124,264],"characteristics":[64],"druggability.The":[66],"aim":[67],"of":[68,96,150,153,165,217,269,314,332,400,430,449,468,488,571,578,616,642,674,698,700],"current":[70],"paper":[71],"2":[73,178],"fold:":[74],"(i)":[75],"propose":[77],"a":[78,175],"computational":[79],"method":[80],"using":[86],"combinations":[87],"parameters":[89,125,149,159],"(MPs)":[90,126],"(ii)":[92],"establish":[94],"which":[95,102,422],"ANN":[97,434,511,555,565,628],"or":[98,135,143,221,498],"RF":[99,207,237,251,277,306,320,385,408,474,533],"procedures":[100],"scoring":[103,647,696],"functions":[104,673,697],"best":[105,274,443,677],"partition":[106,189,326],"target":[110,303,321,411,435,512,534],"space.Classifications":[111],"by":[112,406,668],"Artificial":[113],"Neural":[114],"Networks":[115],"Random":[118],"Forest":[119],"(RF)":[120],"8":[123],"were":[127,233,261,364,685],"performed":[128,208,308,327,394,461,482],"classify":[130,631],"with":[133,283,312,340,361,426,445,464,484,692],"high":[134,633],"low":[136,638],"violator":[137],"scores":[138],"(using":[139],"cutpoints":[140,664,678],"3,":[141],"4":[142,147,157,220,253,387,438,456,478,495,598],"5),":[144],"the":[146,182,188,202,213,228,236,250,257,302,305,319,323,341,384,407,454,462,473,476,522,527,549,560,588,595,623,632,637,640,646,661],"traditional":[148,624],"Lipinski's":[151],"rule":[152],"five":[154],"(Ro5),":[155],"plus":[156],"extra":[158],"(polar":[160],"surface":[161],"area":[162],"(PSA),":[163],"number":[164,268],"rotatable":[166,270],"bonds":[167,271],"rings,":[169],"N":[170],"O":[172],"atoms,":[173],"choice":[176],"between":[177],"alternatives":[179],"lipophilicity,":[181],"distribution":[183],"coefficient":[184,190],"(log":[185,191,365],"D)":[186],"P)":[192],"(Hudson":[193],"et":[194,198,651,654,670],"al.,":[195,199],"(2017),":[196],"Zafar":[197,650],"(2013,":[200],"2016)).For":[201],"molecule":[203],"parameter":[204],"(MP)":[205],"data":[206,304],"better":[209,309],"than":[210,310,584],"ANNs":[211],"log":[214,229,244,247,254,262,281,290,315,369,373,378,388,391,415,439,457,479,496,499,552,563,575,585,591,599,608,643],"D":[215,245,255,291,316,374,416,440,497,539,580,620,644],"model":[216,256,275,460,556,601],"either":[218],"score":[219,222,252,279,324,386,409,413,418,437,455,477,494,550,561,589,597,634],"5":[223,280,325],"was":[224,278],"optimal":[225,596],"compared":[226,520,545,573,621],"P":[230,379,392,459,481,500,554,564,586,593],"model.ANNs":[231],"however,":[232],"superior":[234,376],"models":[238,307,322,393,501,535],"MP":[240,357,667],"sets":[241],"containing":[242],"both":[243,423,706],"P.":[248],"For":[249],"most":[258,285,528],"important":[259,286,519,529,583],"variables":[260,287,358],"D,":[263,282,366,389,600,609],"weight":[265],"(MW)":[266],"(ROT).The":[272],"next":[273],"via":[276],"its":[284],"being":[288,317],"PSA,":[289],"MW,":[293],"according":[294],"mean":[296,343],"decrease":[297,344],"gini":[299,345],"scores.Overall,":[300],"ANNs,":[311],"inclusion":[313],"important.For":[318],"best,":[328],"AUC":[329,397,427,446,465,485],"(95%":[330,398,428,447,466,486],"CI)":[331,399,429,448,467,487],"0.88":[333],"(0.63,":[334],"1.0)":[335],"all":[337,532,537],"3":[338,410,414,508,510,551,562,590],"models;":[339],"higher":[342],"values":[346],"(MDGs)":[347],"attributable":[348,605],"MPs":[350],"(MW,":[351],"NATOM,":[352,367],"ROT,":[353],"PSA":[354,515,617],"Hacceptors,":[355,611],"NRING).The":[356],"then":[359],"chosen":[360],"lower":[362],"MDGs":[363],"NRING,":[368,610],"P,":[370],"Hdonors),":[371],"indicating":[372],"(VIs,":[380],"2.14":[381],">":[382],"1.47).Also":[383],"equally":[395,503],"well,":[396,704],"0.85":[401],"(0.70,":[402],"1.00)":[403],"-closely":[404],"followed":[405],"models,":[412,513],"3(log":[419],"D+log":[420,480,553,592],"P),":[421],"did":[424],"well":[425,504],"0.84":[431],"(0.73,":[432],"0.94).The":[433],"model,":[441,566],"achieved":[442],"classification,":[444],"0.89":[450],"(0.77,":[451],"1.0).In":[452],"contrast":[453,524],"D+Log":[458],"worst,":[463],"0.69":[469],"(0.51,":[470],"0.86).Similarly":[471],"analysis,":[475],"worse":[483],"0.83":[489],"(0.68,":[490],"0.92),":[491],"whilst":[492],"separate":[493],"classified":[502],"(0.85,":[505],"(0.707,":[506],"1.0)).All":[507],"cutpoint":[509],"showed":[514],"be":[517,690],"highly":[518],"MW.In":[523],"MW":[525,547,567],"variable":[530,542],"cutpoints.Log":[538],"greater":[541],"importance":[543],"(VI)":[544],"(17.31":[557],">12.60).Also":[558],"least":[569],"VI":[570,577],"6.46":[572],"P's":[576],"17.15.Log":[579],"more":[582],"model..For":[594],"top":[602],"VIs":[603],"are":[604],"(PSA,":[607],"MW),":[612],"showing":[613],"strong":[614],"influence":[615],"Log":[619],"MW.The":[625],"RP":[626],"rules":[629],"violators":[635],"from":[636],"confirmed":[639,686],"value":[641],"function,":[648],"validating":[649],"al.":[652,655,671],"(2016Zafar":[653],"(":[656],",":[657],"2013)":[658],")":[659],"original":[662],"MC/DA":[663],"each":[666],"Hudson":[669],"(2017).Score":[672],"violations":[675,701],"molecules":[682,710],"associated":[691],"specific":[693],"diseases.Our":[694],"simple":[695],"counts":[699],"partitioned":[702],"chemospace":[703],"identifying":[705],"good/":[707],"poor":[708],"targets.":[712]},"counts_by_year":[],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2020-05-01T00:00:00"}