{"id":"https://openalex.org/W2784252267","doi":"https://doi.org/10.1109/icpr.2018.8545246","title":"Graph Memory Networks for Molecular Activity Prediction","display_name":"Graph Memory Networks for Molecular Activity Prediction","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2784252267","doi":"https://doi.org/10.1109/icpr.2018.8545246","mag":"2784252267"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545246","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1801.02622","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102943268","display_name":"Trang Pham","orcid":"https://orcid.org/0000-0003-3702-7963"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Trang Pham","raw_affiliation_strings":["Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085471517","display_name":"Truyen Tran","orcid":"https://orcid.org/0000-0001-6531-8907"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Truyen Tran","raw_affiliation_strings":["Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045540854","display_name":"Svetha Venkatesh","orcid":"https://orcid.org/0000-0001-8675-6631"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Svetha Venkatesh","raw_affiliation_strings":["Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Pattern Recognition and Data Analytics Deakin University, Geelong, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102943268"],"corresponding_institution_ids":["https://openalex.org/I149704539"],"apc_list":null,"apc_paid":null,"fwci":1.923,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.8719636,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"639","last_page":"644"},"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":0.9987000226974487,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9908000230789185,"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/computer-science","display_name":"Computer science","score":0.7495023012161255},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5478569269180298},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49318522214889526},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4838670492172241},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.48384204506874084},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4809086322784424},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4519365727901459},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34417086839675903}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7495023012161255},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5478569269180298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49318522214889526},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4838670492172241},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.48384204506874084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4809086322784424},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4519365727901459},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34417086839675903},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/icpr.2018.8545246","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545246","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1801.02622","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1801.02622","pdf_url":"https://arxiv.org/pdf/1801.02622","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2784252267","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1801.02622","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:dro.deakin.edu.au:DU:30120222","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402457","display_name":"Deakin Research Online (Deakin University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149704539","host_organization_name":"Deakin University","host_organization_lineage":["https://openalex.org/I149704539"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},{"id":"pmh:oai:figshare.com:article/20776228","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Graph_memory_networks_for_molecular_activity_prediction/20776228","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"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"},{"id":"doi:10.48550/arxiv.1801.02622","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1801.02622","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1801.02622","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1801.02622","pdf_url":"https://arxiv.org/pdf/1801.02622","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320970","display_name":"Deakin University","ror":"https://ror.org/02czsnj07"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2784252267.pdf","grobid_xml":"https://content.openalex.org/works/W2784252267.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1655274992","https://openalex.org/W1793121960","https://openalex.org/W1974166884","https://openalex.org/W1977405994","https://openalex.org/W1988037271","https://openalex.org/W1988195734","https://openalex.org/W2033757486","https://openalex.org/W2116341502","https://openalex.org/W2131744502","https://openalex.org/W2177508090","https://openalex.org/W2290847742","https://openalex.org/W2436108096","https://openalex.org/W2530887700","https://openalex.org/W2919115771","https://openalex.org/W2950621961","https://openalex.org/W2963064784","https://openalex.org/W2963920355","https://openalex.org/W2963984147","https://openalex.org/W6626481562","https://openalex.org/W6638318767","https://openalex.org/W6674330103","https://openalex.org/W6679775712","https://openalex.org/W6685350579","https://openalex.org/W6690815549","https://openalex.org/W6713582119","https://openalex.org/W6719270105","https://openalex.org/W6736371746","https://openalex.org/W6736685754","https://openalex.org/W6743626410","https://openalex.org/W6758327135"],"related_works":["https://openalex.org/W3185204125","https://openalex.org/W2134499763","https://openalex.org/W3128327402","https://openalex.org/W2007477772","https://openalex.org/W2810172011","https://openalex.org/W1278276328","https://openalex.org/W3214213376","https://openalex.org/W3208667955","https://openalex.org/W3000308450","https://openalex.org/W2810631159","https://openalex.org/W3092472376","https://openalex.org/W3089084887","https://openalex.org/W2557260587","https://openalex.org/W2952475472","https://openalex.org/W13840495","https://openalex.org/W3158246141","https://openalex.org/W2035593643","https://openalex.org/W2901287800","https://openalex.org/W3157142061","https://openalex.org/W2895788438"],"abstract_inverted_index":{"Molecular":[0],"activity":[1,206],"prediction":[2],"is":[3,47,164],"critical":[4],"in":[5,37,49,68,118],"drug":[6],"design.":[7],"Machine":[8],"learning":[9,85],"techniques":[10],"such":[11,72],"as":[12,30,64,73,181],"kernel":[13],"methods":[14],"and":[15,39,76,135,194],"random":[16],"forests":[17],"have":[18],"been":[19],"successful":[20],"for":[21,52,192],"this":[22],"task.":[23],"These":[24],"models":[25],"require":[26],"fixed-size":[27,44],"feature":[28],"vectors":[29],"input":[31],"while":[32],"the":[33,58,95,115,144,146,154,179,186,189],"molecules":[34],"are":[35,66],"variable":[36],"size":[38],"structure.":[40],"As":[41],"a":[42,83,109,123,138,174],"result,":[43],"fingerprint":[45],"representation":[46,155],"poor":[48],"handling":[50],"substructures":[51],"large":[53],"molecules.":[54,119],"Here":[55],"we":[56],"approach":[57],"problem":[59],"through":[60,137],"deep":[61],"neural":[62,111],"networks":[63],"they":[65],"flexible":[67],"modeling":[69],"structured":[70],"data":[71],"grids,":[74],"sequences":[75],"graphs.":[77],"We":[78,103,184],"train":[79],"multiple":[80,91,160,170],"BioAssays":[81],"using":[82,173],"multi-task":[84],"framework,":[86],"which":[87],"combines":[88],"information":[89],"from":[90],"sources":[92],"to":[93,113,143,178],"improve":[94],"performance":[96],"of":[97,122,153,156,166,188],"prediction,":[98],"especially":[99],"on":[100,169,197],"small":[101],"datasets.":[102],"propose":[104],"Graph":[105],"Memory":[106],"Network":[107],"(GraphMem),":[108],"memory-augmented":[110],"network":[112],"model":[114,191],"graph":[116],"structure":[117],"GraphMem":[120,163],"consists":[121],"recurrent":[124],"controller":[125,180],"coupled":[126],"with":[127,159],"an":[128,150,182],"external":[129],"memory":[130],"whose":[131],"cells":[132],"dynamically":[133],"interact":[134],"change":[136],"multi-hop":[139],"reasoning":[140],"process.":[141],"Applied":[142],"molecules,":[145],"dynamic":[147],"interactions":[148],"enable":[149],"iterative":[151],"refinement":[152],"molecular":[157],"graphs":[158],"bond":[161],"types.":[162],"capable":[165],"jointly":[167,195],"training":[168,196],"datasets":[171],"by":[172],"specific-task":[175],"query":[176],"fed":[177],"input.":[183],"demonstrate":[185],"effectiveness":[187],"proposed":[190],"separately":[193],"more":[198],"than":[199],"100K":[200],"measurements,":[201],"spanning":[202],"across":[203],"9":[204],"BioAssay":[205],"tests.":[207]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
