{"id":"https://openalex.org/W7225077987","doi":"https://doi.org/10.1201/9781042042159-48","title":"Integrating Artificial Intelligence in Drug Discovery: A Comprehensive Review","display_name":"Integrating Artificial Intelligence in Drug Discovery: A Comprehensive Review","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225077987","doi":"https://doi.org/10.1201/9781042042159-48"},"language":"en","primary_location":{"id":"doi:10.1201/9781042042159-48","doi":"https://doi.org/10.1201/9781042042159-48","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9781042042159-48","pdf_url":null,"source":{"id":"https://openalex.org/S4306463075","display_name":"CRC Press eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320584","host_organization_name":"CRC Press","host_organization_lineage":["https://openalex.org/P4310320584","https://openalex.org/P4310320547"],"host_organization_lineage_names":["CRC Press","Taylor & Francis"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Additive Manufacturing and Emerging Materials Processing","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W7225100919","display_name":"Advances in Additive Manufacturing and Emerging Materials Processing"},"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/A5160326428","display_name":"Tejinder Sharma","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tejinder Sharma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5040589829","display_name":"Narinder Sharma","orcid":"https://orcid.org/0000-0002-4849-9491","observed_orcids":["https://orcid.org/0000-0002-4849-9491"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Narinder Sharma","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":0.91852825,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"240","last_page":"244"},"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/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":1.0,"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/T11948","display_name":"Machine Learning in Materials Science","score":9.999999747378752e-05,"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/T12534","display_name":"Protein Degradation and Inhibitors","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/artificial-intelligence-in-drug-discovery","display_name":"artificial intelligence in drug discovery","score":0.9710000157356262},{"id":"https://openalex.org/keywords/generative-models","display_name":"generative models","score":0.7120000123977661},{"id":"https://openalex.org/keywords/target-identification","display_name":"target identification","score":0.5580000281333923},{"id":"https://openalex.org/keywords/compound-screening","display_name":"compound screening","score":0.9210000038146973},{"id":"https://openalex.org/keywords/de-novo-molecular-design","display_name":"de novo molecular design","score":0.9940000176429749},{"id":"https://openalex.org/keywords/admet-prediction","display_name":"ADMET prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/protein-structure-modeling","display_name":"protein structure modeling","score":0.9909999966621399},{"id":"https://openalex.org/keywords/deepchem","display_name":"DeepChem","score":0.6399999856948853},{"id":"https://openalex.org/keywords/rdkit","display_name":"RDKit","score":0.7860000133514404},{"id":"https://openalex.org/keywords/moleculenet","display_name":"MoleculeNet","score":0.9509999752044678},{"id":"https://openalex.org/keywords/data-quality","display_name":"data quality","score":0.6589999794960022},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.8709999918937683},{"id":"https://openalex.org/keywords/regulatory-acceptance","display_name":"regulatory acceptance","score":0.949999988079071},{"id":"https://openalex.org/keywords/physics-informed-modeling","display_name":"physics-informed modeling","score":0.9539999961853027},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.9990000128746033}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6527000069618225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845000147819519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5501000285148621},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.5170000195503235},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4059999883174896},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C2989108626","wikidata":"https://www.wikidata.org/wiki/Q904407","display_name":"Drug target","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C103637391","wikidata":"https://www.wikidata.org/wiki/Q5308921","display_name":"Drug repositioning","level":3,"score":0.3237000107765198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3203999996185303},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.29580000042915344},{"id":"https://openalex.org/C64903051","wikidata":"https://www.wikidata.org/wiki/Q2198549","display_name":"Drug development","level":3,"score":0.26930001378059387}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1201/9781042042159-48","doi":"https://doi.org/10.1201/9781042042159-48","is_oa":false,"landing_page_url":"https://doi.org/10.1201/9781042042159-48","pdf_url":null,"source":{"id":"https://openalex.org/S4306463075","display_name":"CRC Press eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320584","host_organization_name":"CRC Press","host_organization_lineage":["https://openalex.org/P4310320584","https://openalex.org/P4310320547"],"host_organization_lineage_names":["CRC Press","Taylor & Francis"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Additive Manufacturing and Emerging Materials Processing","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"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/W7218463398","https://openalex.org/W7117128651","https://openalex.org/W7154696209","https://openalex.org/W7162668940","https://openalex.org/W7204498758","https://openalex.org/W7208712050","https://openalex.org/W7133222643","https://openalex.org/W4413148963","https://openalex.org/W7196930441","https://openalex.org/W7109612457"],"abstract_inverted_index":{"Artificial":[0],"Intelligence":[1],"(AI)":[2],"is":[3,81],"changing":[4],"drug":[5,118],"discovery":[6],"through":[7],"data-driven":[8],"and":[9,36,52,58,63,98,106,115],"generative":[10,51],"methods":[11],"throughout":[12],"the":[13,21,74,83,88],"pharmaceutical":[14],"pipeline.":[15],"The":[16],"literature":[17],"review":[18],"points":[19],"to":[20],"recent":[22],"developments":[23],"in":[24,113],"AI-driven":[25],"target":[26],"discovery,":[27],"compound":[28],"screening,":[29],"de":[30],"novo":[31],"molecular":[32,57],"design,":[33],"ADMET":[34],"prediction,":[35],"protein":[37],"structure":[38],"modeling.":[39],"It":[40],"describes":[41],"how":[42],"traditional":[43],"machine-learning":[44],"models":[45,54],"have":[46],"been":[47],"replaced":[48],"with":[49,94],"contemporary":[50],"multimodel":[53],"that":[55],"combine":[56],"omics":[59],"data.":[60],"Popular":[61],"tools":[62],"benches":[64],"like":[65],"DeepChem,":[66],"RDKit,":[67],"or":[68],"MoleculeNet":[69],"also":[70],"are":[71,79,91],"mentioned.":[72],"Although":[73],"speed":[75],"at":[76],"which":[77,109],"drugs":[78],"developed":[80],"on":[82],"rise,":[84],"as":[85,87],"well":[86],"cost,":[89],"there":[90],"still":[92],"difficulties":[93],"data":[95],"quality,":[96],"interpretability,":[97],"regulatory":[99],"acceptance.":[100],"Future":[101],"directions":[102],"include":[103],"physics-informed":[104],"modeling":[105],"explainable":[107],"AI,":[108],"would":[110],"be":[111],"helpful":[112],"reliable":[114],"clinically":[116],"relevant":[117],"discovery.":[119]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-11-03T00:00:00"}