{"id":"https://openalex.org/W7225466748","doi":"https://doi.org/10.5281/zenodo.24033349","title":"Generative and Deep Learning In Bioinformatics: A Review of Protein Structure Prediction, Sequence Representation Learning and AI-Assisted Drug Discovery","display_name":"Generative and Deep Learning In Bioinformatics: A Review of Protein Structure Prediction, Sequence Representation Learning and AI-Assisted Drug Discovery","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225466748","doi":"https://doi.org/10.5281/zenodo.24033349"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24033349","doi":"https://doi.org/10.5281/zenodo.24033349","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24033349","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24033349","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161250096","display_name":"Tirandaj Fatima Khalid","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tirandaj Fatima Khalid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161246222","display_name":"Prof. S. S. Bhor","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prof. S. S. Bhor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161211958","display_name":"Prof. M. C. Kolse","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prof. M. C. Kolse","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5161196024","display_name":"Prof. S. K. Said","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prof. S. K. Said","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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.8628000020980835,"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"}},"topics":[{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.8628000020980835,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.07280000299215317,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.04830000177025795,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/protein-structure-prediction","display_name":"protein structure prediction","score":0.8859999775886536},{"id":"https://openalex.org/keywords/generative-models","display_name":"generative models","score":0.8149999976158142},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.7319999933242798},{"id":"https://openalex.org/keywords/sequence-representation-learning","display_name":"sequence representation learning","score":0.8650000095367432},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.5920000076293945},{"id":"https://openalex.org/keywords/variational-autoencoder","display_name":"variational autoencoder","score":0.9179999828338623},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.9909999966621399},{"id":"https://openalex.org/keywords/diffusion-models","display_name":"diffusion models","score":0.9980000257492065},{"id":"https://openalex.org/keywords/protein-complexes","display_name":"protein complexes","score":0.7129999995231628},{"id":"https://openalex.org/keywords/virtual-screening","display_name":"virtual screening","score":0.6990000009536743},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"drug discovery","score":0.7400000095367432},{"id":"https://openalex.org/keywords/variant-effect-prediction","display_name":"variant effect prediction","score":0.8069999814033508},{"id":"https://openalex.org/keywords/biosecurity","display_name":"biosecurity","score":0.6290000081062317},{"id":"https://openalex.org/keywords/responsible-use-of-ai","display_name":"responsible AI use","score":0.6420000195503235},{"id":"https://openalex.org/keywords/experimental-validation","display_name":"experimental validation","score":0.45399999618530273}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7232999801635742},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6654999852180481},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.652400016784668},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.644599974155426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5873000025749207},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5130000114440918},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4025000035762787},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3774000108242035},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3034999966621399},{"id":"https://openalex.org/C10010492","wikidata":"https://www.wikidata.org/wiki/Q3142557","display_name":"Protein sequencing","level":4,"score":0.301800012588501},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C18051474","wikidata":"https://www.wikidata.org/wiki/Q899656","display_name":"Protein structure prediction","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25929999351501465}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24033349","doi":"https://doi.org/10.5281/zenodo.24033349","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24033349","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5281/zenodo.24033350","doi":"https://doi.org/10.5281/zenodo.24033350","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24033350","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24033349","doi":"https://doi.org/10.5281/zenodo.24033349","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24033349","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7172247553","https://openalex.org/W7170043904","https://openalex.org/W4385661734","https://openalex.org/W7149218000","https://openalex.org/W7212455522","https://openalex.org/W4365792257","https://openalex.org/W4200382517","https://openalex.org/W4367301305","https://openalex.org/W7203670011","https://openalex.org/W6959814139"],"abstract_inverted_index":{"Artificial":[0],"intelligence,":[1],"and":[2,22,31,43,66,72,78,105,108,115,138,147,159,174,181,192,207],"generative":[3,44,124],"modelling":[4,145],"in":[5,11,48,64,90,127,151],"particular,":[6],"is":[7],"creating":[8],"new":[9],"opportunities":[10],"bioinformatics.":[12],"Conventional":[13],"computational":[14,29,152],"biology":[15],"relied":[16],"on":[17,87,95],"physics-based":[18],"simulation,":[19],"sequence":[20,98],"alignment":[21],"manual":[23],"feature":[24],"engineering,":[25],"each":[26],"limited":[27],"by":[28],"cost":[30],"the":[32,121,128,141,168,175],"need":[33],"for":[34,205],"expert":[35],"interpretation.":[36],"This":[37],"paper":[38,119],"reviews":[39,84,148],"how":[40],"deep":[41,58],"learning":[42,59,86],"models":[45,93,125,137],"are":[46,194,202],"used":[47,126],"bioinformatics":[49],"research.":[50],"It":[51,82,162],"first":[52],"examines":[53],"protein":[54],"structure":[55],"prediction,":[56],"where":[57],"achieved":[60],"a":[61,187],"step":[62],"change":[63],"accuracy":[65],"extended":[67],"to":[68],"complexes":[69],"of":[70,123,143,171,177,189],"biomolecules,":[71],"discusses":[73,163],"what":[74],"such":[75,112,155],"predictions":[76],"can":[77],"cannot":[79],"tell":[80],"researchers.":[81],"then":[83],"representation":[85],"biological":[88],"sequences,":[89],"which":[91],"transformer":[92],"trained":[94],"large":[96],"public":[97],"databases":[99],"learn":[100],"representations":[101],"that":[102,199],"capture":[103],"structural":[104],"evolutionary":[106],"signal":[107],"support":[109],"downstream":[110],"tasks":[111],"as":[113,156],"property":[114],"variant-effect":[116],"prediction.":[117],"The":[118,196],"surveys":[120],"families":[122,191],"field,":[129],"including":[130,179],"variational":[131],"autoencoders,":[132],"graph":[133],"neural":[134],"networks,":[135],"diffusion":[136],"transformers,":[139],"at":[140],"level":[142],"their":[144],"assumptions,":[146],"published":[149],"applications":[150],"drug":[153],"discovery":[154],"virtual":[157],"screening":[158],"candidate":[160],"prioritization.":[161],"data":[164],"resources,":[165],"evaluation":[166],"practice,":[167],"central":[169],"role":[170],"experimental":[172],"validation,":[173],"governance":[176],"research,":[178],"responsible-use":[180],"biosecurity":[182],"frameworks.":[183],"Research":[184],"gaps,":[185],"objectives,":[186],"comparison":[188],"model":[190],"challenges":[193],"presented.":[195],"review":[197],"concludes":[198],"these":[200],"methods":[201],"decision-support":[203],"tools":[204],"researchers":[206],"do":[208],"not":[209],"replace":[210],"laboratory":[211],"experimentation":[212],"or":[213],"institutional":[214],"oversight.":[215]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}