{"id":"https://openalex.org/W6949547121","doi":"https://doi.org/10.5281/zenodo.15300665","title":"The First Report on Machine Learning-Driven q-RASAR Modeling for Predicting \u03b17nACh Receptor Agonists: A Computational Approach for Anti-Alzheimer's Drug Discovery","display_name":"The First Report on Machine Learning-Driven q-RASAR Modeling for Predicting \u03b17nACh Receptor Agonists: A Computational Approach for Anti-Alzheimer's Drug Discovery","publication_year":2025,"publication_date":"2025-04-29","ids":{"openalex":"https://openalex.org/W6949547121","doi":"https://doi.org/10.5281/zenodo.15300665"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.15300665","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15300665","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,"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":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.15300665","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Vinay, Kumar","orcid":"https://orcid.org/0000-0002-6809-7633"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vinay, Kumar","raw_affiliation_strings":["Jadavpur University, India"],"affiliations":[{"raw_affiliation_string":"Jadavpur University, India","institution_ids":["https://openalex.org/I170979836"]}]},{"author_position":"last","author":{"id":null,"display_name":"Kunal, Roy","orcid":"https://orcid.org/0000-0003-4486-8074"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kunal, Roy","raw_affiliation_strings":["Jadavpur University, India"],"affiliations":[{"raw_affiliation_string":"Jadavpur University, India","institution_ids":["https://openalex.org/I170979836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170979836"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.6442999839782715},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.5217999815940857},{"id":"https://openalex.org/keywords/applicability-domain","display_name":"Applicability domain","score":0.46540001034736633},{"id":"https://openalex.org/keywords/quantitative-structure\u2013activity-relationship","display_name":"Quantitative structure\u2013activity relationship","score":0.45210000872612},{"id":"https://openalex.org/keywords/docking","display_name":"Docking (animal)","score":0.44999998807907104},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.42910000681877136},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.39239999651908875},{"id":"https://openalex.org/keywords/drug-target","display_name":"Drug target","score":0.37380000948905945},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.35670000314712524}],"concepts":[{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.6442999839782715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6116999983787537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5669999718666077},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.48030000925064087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4713999927043915},{"id":"https://openalex.org/C107908354","wikidata":"https://www.wikidata.org/wiki/Q4781456","display_name":"Applicability domain","level":3,"score":0.46540001034736633},{"id":"https://openalex.org/C164126121","wikidata":"https://www.wikidata.org/wiki/Q766383","display_name":"Quantitative structure\u2013activity relationship","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C41685203","wikidata":"https://www.wikidata.org/wiki/Q1974042","display_name":"Docking (animal)","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C2989108626","wikidata":"https://www.wikidata.org/wiki/Q904407","display_name":"Drug target","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C137866125","wikidata":"https://www.wikidata.org/wiki/Q4299308","display_name":"Modelling biological systems","level":3,"score":0.32989999651908875},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C80161118","wikidata":"https://www.wikidata.org/wiki/Q408520","display_name":"Acetylcholine receptor","level":3,"score":0.30970001220703125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30480000376701355},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.29319998621940613},{"id":"https://openalex.org/C2779570518","wikidata":"https://www.wikidata.org/wiki/Q412256","display_name":"Nicotinic acetylcholine receptor","level":4,"score":0.273499995470047},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C183696295","wikidata":"https://www.wikidata.org/wiki/Q2487696","display_name":"Biochemical engineering","level":1,"score":0.2619999945163727},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C3019813237","wikidata":"https://www.wikidata.org/wiki/Q65089264","display_name":"Model validation","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C164923092","wikidata":"https://www.wikidata.org/wiki/Q3705921","display_name":"Molecular descriptor","level":3,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.15300665","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15300665","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,"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":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.15300665","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15300665","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,"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":"dataset"},"sustainable_development_goals":[{"score":0.482615202665329,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"the":[1,13,42,48,100,115,119,131,190,202,230,262],"present":[2],"research,":[3],"we":[4,74,166],"have":[5,75,167],"explored":[6],"an":[7],"advanced":[8],"computational":[9,251],"technique":[10],"known":[11],"as":[12,159,215],"quantitative":[14],"Read-Across":[15],"Structure-Activity":[16],"Relationship":[17],"(q-RASAR)":[18],"framework,":[19],"leveraging":[20],"machine":[21],"learning":[22],"(ML)":[23],"to":[24,32,63,113,155,186],"enhance":[25,164],"predictive":[26,254],"precision.":[27],"Our":[28],"primary":[29],"objective":[30],"was":[31,110,149],"develop":[33],"a":[34,54,77,86,240,257],"statistically":[35],"robust":[36],"ML-based":[37,80,171],"q-RASAR":[38,81,108,172],"model":[39,84,109],"for":[40,151,182,218,243,260,268],"predicting":[41],"agonistic":[43,141],"activity":[44],"of":[45,89,118,133,146,205,210,229,264],"compounds":[46],"targeting":[47],"\u03b17":[49],"nicotinic":[50],"acetylcholine":[51],"(\u03b17nACh)":[52],"receptor,":[53],"critical":[55],"target":[56,191],"in":[57,66],"Alzheimer's":[58],"disease":[59],"(AD)":[60],"therapy":[61],"due":[62],"its":[64],"role":[65],"cognitive":[67],"function":[68],"and":[69,94,178,193,212],"neuroprotection.":[70],"To":[71,162],"achieve":[72],"this,":[73],"developed":[76,107,168],"well-validated":[78],"univariate":[79],"MLR":[82],"regression":[83],"using":[85],"large":[87],"dataset":[88],"1,727":[90],"structurally":[91,134],"diverse":[92],"heterocyclic":[93],"aromatic":[95],"hydrocarbon":[96],"compounds,":[97,129],"sourced":[98],"from":[99,198],"freely":[101],"accessible":[102],"Binding":[103],"Database":[104],"(www.bindingdb.org).":[105],"The":[106,195],"further":[111,152,163],"applied":[112],"assess":[114],"applicability":[116],"domain":[117],"Mcule":[120],"database":[121],"(accessible":[122],"at":[123],"https://mcule.com/database/),":[124],"which":[125],"comprises":[126],"1,91,94,405":[127],"chemical":[128],"facilitating":[130],"identification":[132],"relevant":[135],"candidates":[136],"with":[137,253],"potential":[138,158,219],"\u03b17nACh":[139],"receptor":[140],"activity.":[142],"A":[143],"graded":[144],"set":[145],"candidate":[147],"molecules":[148],"proposed":[150],"experimental":[153],"validation":[154],"evaluate":[156],"their":[157],"anti-Alzheimer\u2019s":[160,220],"agents.":[161,221],"predictability,":[165],"various":[169],"other":[170],"models.":[173],"Furthermore,":[174],"molecular":[175,231],"docking":[176],"analysis":[177],"Molecular":[179],"dynamic":[180],"simulation":[181],"100ns":[183],"were":[184],"conducted":[185],"investigate":[187],"interactions":[188],"between":[189],"protein":[192],"ligand.":[194],"insights":[196],"gained":[197],"this":[199],"study":[200],"underscore":[201],"crucial":[203],"roles":[204],"hydrophobicity,":[206],"electronic":[207],"effects,":[208],"degree":[209],"ionization,":[211],"steric":[213],"factors":[214],"key":[216],"determinants":[217,232],"This":[222,248],"research":[223],"not":[224],"only":[225],"advances":[226],"our":[227],"understanding":[228],"influencing":[233],"CNS":[234],"drug":[235],"permeability":[236],"but":[237],"also":[238],"provides":[239],"valuable":[241,258],"framework":[242,259],"designing":[244],"next-generation":[245],"anti-Alzheimer's":[246],"agent.":[247],"approach":[249],"integrates":[250],"efficiency":[252],"accuracy,":[255],"offering":[256],"accelerating":[261],"discovery":[263],"novel":[265],"therapeutic":[266],"leads":[267],"AD.":[269]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
