{"id":"https://openalex.org/W7214968172","doi":"https://doi.org/10.58532/nbennur3333c2","title":"TARGET IDENTIFICATION AND VALIDATION","display_name":"TARGET IDENTIFICATION AND VALIDATION","publication_year":2026,"publication_date":"2026-09-01","ids":{"openalex":"https://openalex.org/W7214968172","doi":"https://doi.org/10.58532/nbennur3333c2"},"language":"en","primary_location":{"id":"doi:10.58532/nbennur3333c2","doi":"https://doi.org/10.58532/nbennur3333c2","is_oa":false,"landing_page_url":"https://doi.org/10.58532/nbennur3333c2","pdf_url":null,"source":{"id":"https://openalex.org/S7407089365","display_name":"Iterative International Publishers eBooks","issn_l":null,"issn":null,"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":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IIP Series","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W7214952878","display_name":"AI-DRIVEN DRUG DEVELOPMENT: FROM CODE TO CLINIC"},"type":"book-chapter","is_traditional":true,"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/A5060702216","display_name":"Shipra Thapar","orcid":"https://orcid.org/0000-0003-4030-081X","observed_orcids":["https://orcid.org/0000-0003-4030-081X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shipra Thapar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5153557862","display_name":"Prof. (Dr.) Dinesh Kumar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prof. (Dr.) Dinesh Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"31","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.0010999999940395355,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":9.999999747378752e-05,"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/drug-target-identification","display_name":"drug target identification","score":0.7369999885559082},{"id":"https://openalex.org/keywords/drug-target-validation","display_name":"drug target validation","score":0.8479999899864197},{"id":"https://openalex.org/keywords/artificial-intelligence-in-drug-discovery","display_name":"artificial intelligence in drug discovery","score":0.9399999976158142},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.6970000267028809},{"id":"https://openalex.org/keywords/multi-omics-integration","display_name":"multi-omics integration","score":0.8100000023841858},{"id":"https://openalex.org/keywords/druggability-prediction","display_name":"druggability prediction","score":0.7760000228881836},{"id":"https://openalex.org/keywords/target-ranking","display_name":"target ranking","score":0.6850000023841858},{"id":"https://openalex.org/keywords/protein-protein-interaction-network","display_name":"protein-protein interaction network","score":0.7179999947547913},{"id":"https://openalex.org/keywords/gene-regulatory-networks","display_name":"gene regulatory networks","score":0.925000011920929},{"id":"https://openalex.org/keywords/crispr-cas-genome-editing","display_name":"CRISPR/Cas genome editing","score":0.7730000019073486},{"id":"https://openalex.org/keywords/rna-interference","display_name":"RNA interference","score":0.9750000238418579},{"id":"https://openalex.org/keywords/organoids","display_name":"organoids","score":0.7639999985694885},{"id":"https://openalex.org/keywords/patient-derived-models","display_name":"patient-derived models","score":0.9430000185966492},{"id":"https://openalex.org/keywords/precision-medicine","display_name":"precision medicine","score":0.906000018119812},{"id":"https://openalex.org/keywords/digital-twin","display_name":"digital twin","score":0.9259999990463257},{"id":"https://openalex.org/keywords/generative-ai","display_name":"generative AI","score":0.49300000071525574}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6765000224113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6579999923706055},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6028000116348267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5936999917030334},{"id":"https://openalex.org/C10679952","wikidata":"https://www.wikidata.org/wiki/Q5308937","display_name":"Druggability","level":3,"score":0.5849999785423279},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.5611000061035156},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C152662350","wikidata":"https://www.wikidata.org/wiki/Q815297","display_name":"Systems biology","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C2989108626","wikidata":"https://www.wikidata.org/wiki/Q904407","display_name":"Drug target","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C201797286","wikidata":"https://www.wikidata.org/wiki/Q4914986","display_name":"Biological data","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.36070001125335693},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C137866125","wikidata":"https://www.wikidata.org/wiki/Q4299308","display_name":"Modelling biological systems","level":3,"score":0.33059999346733093},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C124535831","wikidata":"https://www.wikidata.org/wiki/Q4915074","display_name":"Biomarker discovery","level":4,"score":0.295199990272522},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2892000079154968},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.26829999685287476},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C67339327","wikidata":"https://www.wikidata.org/wiki/Q1502576","display_name":"Gene regulatory network","level":4,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.58532/nbennur3333c2","doi":"https://doi.org/10.58532/nbennur3333c2","is_oa":false,"landing_page_url":"https://doi.org/10.58532/nbennur3333c2","pdf_url":null,"source":{"id":"https://openalex.org/S7407089365","display_name":"Iterative International Publishers eBooks","issn_l":null,"issn":null,"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":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IIP Series","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.9476000070571899}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7154939658","https://openalex.org/W7213557145","https://openalex.org/W4411213901","https://openalex.org/W7170036405","https://openalex.org/W4384831976","https://openalex.org/W7203891806","https://openalex.org/W7119829032","https://openalex.org/W4410201938","https://openalex.org/W4405745591","https://openalex.org/W7155182427"],"abstract_inverted_index":{"Target":[0,247],"identification":[1,76,142,210],"and":[2,41,51,70,85,108,120,143,183,193,209,218,236,259,276,288,315,351,372,392,411,422],"validation":[3,248,266,310,388],"represent":[4],"critical":[5],"early":[6],"stages":[7,323],"in":[8,151,337],"the":[9,54,87,95,146,189,255,317,331],"drug":[10,153,196,325],"discovery":[11,30,96,224,421],"pipeline,":[12],"determining":[13],"whether":[14],"a":[15,134,241],"biological":[16,57,83,214,227,256,292,412],"molecule,":[17],"pathway,":[18],"or":[19],"mechanism":[20],"has":[21,31,73],"sufficient":[22],"therapeutic":[23,99,260],"potential":[24,195,359],"for":[25,140],"further":[26,221,418],"development.":[27,154,326],"Traditionally,":[28],"target":[29,75,141,201,223,338,344,420],"relied":[32],"on":[33],"experimental":[34,305,387],"approaches,":[35,165],"genetic":[36,374],"studies,":[37,278],"molecular":[38,118],"biology":[39,72,217],"techniques,":[40],"disease-based":[42],"investigations;":[43],"however,":[44],"these":[45,377],"methods":[46],"are":[47,124,415],"often":[48],"time-consuming,":[49],"resource-intensive,":[50],"limited":[52],"by":[53,77,225],"complexity":[55],"of":[56,61,81,89,97,103,137,148,206,211,244,262,299,319,324,333],"systems.":[58],"The":[59,297],"integration":[60],"artificial":[62],"intelligence":[63,150],"(AI),":[64],"machine":[65,112],"learning":[66,113],"(ML),":[67],"multi-omics":[68],"analysis,":[69,280],"computational":[71,149,295],"transformed":[74],"enabling":[78],"rapid":[79],"analysis":[80,102],"large-scale":[82],"datasets":[84],"improving":[86],"prediction":[88,205,301],"disease-associated":[90],"targets.":[91,197,264],"AI-driven":[92,354],"approaches":[93,199,220,336],"facilitate":[94],"novel":[98],"targets":[100],"through":[101],"genomic,":[104],"transcriptomic,":[105],"proteomic,":[106],"metabolomic,":[107],"clinical":[109],"data,":[110],"while":[111],"models":[114,302,414],"identify":[115],"hidden":[116],"patterns,":[117],"relationships,":[119],"disease":[121,245],"mechanisms":[122],"that":[123,312],"difficult":[125],"to":[126,191,239,253,381,417],"recognize":[127],"using":[128],"conventional":[129],"approaches.":[130],"This":[131],"chapter":[132,329],"provides":[133],"comprehensive":[135],"overview":[136],"AI-enabled":[138],"strategies":[139,355],"validation,":[144,339],"emphasizing":[145],"role":[147],"modern":[152],"AI":[155,178],"paradigms,":[156],"including":[157,268,340],"supervised":[158],"learning,":[159,161,163,167,169,174,176,410],"unsupervised":[160],"semi-supervised":[162],"Bayesian":[164],"ensemble":[166],"deep":[168],"graph":[170],"neural":[171],"networks,":[172,228,233],"reinforcement":[173],"transfer":[175],"explainable":[177],"(XAI),":[179],"natural":[180],"language":[181],"processing,":[182],"foundation":[184,413],"models,":[185,283,285],"have":[186,356],"significantly":[187],"expanded":[188],"ability":[190],"prioritize":[192],"evaluate":[194],"These":[198],"support":[200],"ranking,":[202],"druggability":[203],"assessment,":[204],"target\u2013disease":[207],"associations,":[208],"clinically":[212],"relevant":[213],"pathways.":[215],"Systems":[216],"network-based":[219],"enhance":[222,419],"integrating":[226],"protein\u2013protein":[229],"interactions,":[230],"gene":[231,274],"regulatory":[232,390],"metabolic":[234,370],"pathways,":[235],"pharmacological":[237],"information":[238],"provide":[240,291],"holistic":[242],"understanding":[243],"mechanisms.":[246],"remains":[249],"an":[250,308],"essential":[251],"step":[252],"confirm":[254],"relevance,":[257],"safety,":[258],"feasibility":[261],"identified":[263],"Experimental":[265],"strategies,":[267],"CRISPR/Cas-based":[269],"genome":[270],"editing,":[271],"RNA":[272],"interference,":[273],"knockout":[275],"knockdown":[277],"overexpression":[279],"organoids,":[281],"patient-derived":[282],"animal":[284],"functional":[286],"assays,":[287],"high-content":[289],"screening,":[290],"evidence":[293],"supporting":[294],"predictions.":[296],"combination":[298],"AI-based":[300],"with":[303],"advanced":[304],"platforms":[306],"creates":[307],"integrated":[309],"framework":[311],"improves":[313],"accuracy":[314],"reduces":[316],"risk":[318],"failure":[320],"during":[321],"later":[322],"Furthermore,":[327],"this":[328],"discusses":[330],"application":[332],"precision":[334],"medicine":[335],"digital":[341],"twins,":[342],"biomarker-guided":[343],"selection,":[345],"patient":[346],"stratification,":[347],"AI-assisted":[348],"personalized":[349],"therapeutics,":[350],"target-to-drug":[352],"translation.":[353],"demonstrated":[357],"significant":[358],"across":[360],"oncology,":[361],"neurological":[362],"disorders,":[363,369],"infectious":[364],"diseases,":[365,367,371],"cardiovascular":[366],"autoimmune":[368],"rare":[373],"disorders.":[375],"Despite":[376],"advances,":[378],"challenges":[379],"related":[380],"data":[382],"quality,":[383],"model":[384],"interpretability,":[385],"bias,":[386],"limitations,":[389],"requirements,":[391],"ethical":[393],"considerations":[394],"remain":[395],"important":[396],"barriers.":[397],"Future":[398],"developments":[399],"involving":[400],"generative":[401],"AI,":[402,406],"autonomous":[403],"laboratories,":[404],"causal":[405],"quantum":[407],"computing,":[408],"federated":[409],"expected":[416],"validation.":[423]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-10-01T00:00:00"}