{"id":"https://openalex.org/W4206570319","doi":"https://doi.org/10.1109/bibm52615.2021.9669671","title":"Gradient-Norm Based Attentive Loss for Molecular Property Prediction","display_name":"Gradient-Norm Based Attentive Loss for Molecular Property Prediction","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4206570319","doi":"https://doi.org/10.1109/bibm52615.2021.9669671"},"language":"en","primary_location":{"id":"doi:10.1109/bibm52615.2021.9669671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669671","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5078917190","display_name":"Hehuan Ma","orcid":"https://orcid.org/0000-0002-5971-0053"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hehuan Ma","raw_affiliation_strings":["University of Texas at Arlington, Arlington, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington, Arlington, United States","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767600","display_name":"Yu Rong","orcid":"https://orcid.org/0000-0001-7387-302X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Rong","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100387883","display_name":"Boyang Liu","orcid":"https://orcid.org/0000-0003-0341-309X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boyang Liu","raw_affiliation_strings":["Carroll Senior High School Southlake, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carroll Senior High School Southlake, United States","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008281887","display_name":"Yuzhi Guo","orcid":"https://orcid.org/0000-0002-8993-1818"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuzhi Guo","raw_affiliation_strings":["University of Texas at Arlington, Arlington, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington, Arlington, United States","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018185046","display_name":"Chaochao Yan","orcid":"https://orcid.org/0000-0001-8370-0966"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaochao Yan","raw_affiliation_strings":["University of Texas at Arlington, Arlington, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington, Arlington, United States","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["University of Texas at Arlington, Arlington, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington, Arlington, United States","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8096,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71023748,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"497","last_page":"502"},"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":0.9991999864578247,"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":0.9672999978065491,"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":0.7580738067626953},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.7017044425010681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6393933296203613},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6022889018058777},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5876531004905701},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5430478453636169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5412194132804871},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4962618947029114},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4865978956222534},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.47739237546920776},{"id":"https://openalex.org/keywords/weight-distribution","display_name":"Weight distribution","score":0.45898258686065674},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.43370530009269714},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4260016083717346},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3985351324081421},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34917396306991577},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2961343824863434},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17197975516319275},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10035613179206848}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7580738067626953},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.7017044425010681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6393933296203613},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6022889018058777},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5876531004905701},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5430478453636169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5412194132804871},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4962618947029114},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4865978956222534},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.47739237546920776},{"id":"https://openalex.org/C25432639","wikidata":"https://www.wikidata.org/wiki/Q3711790","display_name":"Weight distribution","level":2,"score":0.45898258686065674},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.43370530009269714},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4260016083717346},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3985351324081421},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34917396306991577},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2961343824863434},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17197975516319275},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10035613179206848},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm52615.2021.9669671","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669671","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.41999998688697815,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1596986901","https://openalex.org/W1686810756","https://openalex.org/W2076498053","https://openalex.org/W2096541451","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2136922672","https://openalex.org/W2163605009","https://openalex.org/W2406943157","https://openalex.org/W2461470610","https://openalex.org/W2606780347","https://openalex.org/W2749279690","https://openalex.org/W2888164077","https://openalex.org/W2913015533","https://openalex.org/W2962711740","https://openalex.org/W2962721361","https://openalex.org/W2963017945","https://openalex.org/W2963113370","https://openalex.org/W2963351448","https://openalex.org/W2966357564","https://openalex.org/W2970941190","https://openalex.org/W2973114758","https://openalex.org/W3005552578","https://openalex.org/W3019905301","https://openalex.org/W3034231628","https://openalex.org/W3095883070","https://openalex.org/W3113037084","https://openalex.org/W3114373613","https://openalex.org/W3126316459","https://openalex.org/W3127609309","https://openalex.org/W3127794961","https://openalex.org/W3165246274","https://openalex.org/W4297733535","https://openalex.org/W6635815790","https://openalex.org/W6637373629","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6684191040","https://openalex.org/W6736685754","https://openalex.org/W6747954111","https://openalex.org/W6754929296","https://openalex.org/W6764733053","https://openalex.org/W6772452955","https://openalex.org/W6778953688","https://openalex.org/W6784614190","https://openalex.org/W6795757086"],"related_works":["https://openalex.org/W2591781490","https://openalex.org/W4289238878","https://openalex.org/W2582753012","https://openalex.org/W2949288209","https://openalex.org/W4301387403","https://openalex.org/W4300101497","https://openalex.org/W2379797514","https://openalex.org/W4287364960","https://openalex.org/W2391763874","https://openalex.org/W2039764764"],"abstract_inverted_index":{"Molecular":[0],"property":[1],"prediction":[2,121,146],"is":[3,34,55,83,116,135],"one":[4],"fundamental":[5],"yet":[6],"challenging":[7],"task":[8],"for":[9,102,128,148],"drug":[10],"discovery.":[11],"Many":[12],"studies":[13],"have":[14],"addressed":[15],"this":[16],"problem":[17],"by":[18,85],"designing":[19],"deep":[20],"learning":[21],"algorithms,":[22],"e.g.,":[23],"sequence-based":[24],"models":[25,122],"and":[26,44,51,132,151],"graph-based":[27],"models.":[28],"However,":[29],"the":[30,48,52,65,76,86,108,120,129,145],"underlying":[31],"data":[32,104],"distribution":[33,54],"rarely":[35],"explored.":[36],"We":[37],"discover":[38],"that":[39,71,138],"there":[40],"exist":[41],"easy":[42],"samples":[43,46],"hard":[45],"in":[47],"molecule":[49],"datasets,":[50],"overall":[53],"usually":[56],"imbalanced.":[57],"Current":[58],"research":[59],"mainly":[60],"treats":[61],"them":[62],"equally":[63],"during":[64],"model":[66],"training,":[67],"while":[68],"we":[69,90],"believe":[70],"they":[72],"shall":[73],"not":[74],"share":[75],"same":[77],"weights":[78],"since":[79],"neural":[80],"networks":[81],"training":[82],"dominated":[84],"majority":[87],"class.":[88],"Therefore,":[89],"propose":[91],"to":[92,97,107,123],"utilize":[93],"a":[94,99],"self-attention":[95],"mechanism":[96],"generate":[98],"learnable":[100],"weight":[101],"each":[103],"sample":[105],"according":[106],"associated":[109],"gradient":[110],"norm.":[111],"The":[112],"learned":[113],"attention":[114],"value":[115],"then":[117],"embedded":[118],"into":[119],"construct":[124],"an":[125],"attentive":[126],"loss":[127],"network":[130],"updating":[131],"back-propagation.":[133],"It":[134],"empirically":[136],"demonstrated":[137],"our":[139],"proposed":[140],"method":[141],"can":[142],"consistently":[143],"boost":[144],"performance":[147],"both":[149],"classification":[150],"regression":[152],"tasks.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}