{"id":"https://openalex.org/W4405427572","doi":"https://doi.org/10.1145/3698587.3701429","title":"DynPred: A Multimodal Deep Learning Approach for Protein Dynamics Prediction","display_name":"DynPred: A Multimodal Deep Learning Approach for Protein Dynamics Prediction","publication_year":2024,"publication_date":"2024-11-22","ids":{"openalex":"https://openalex.org/W4405427572","doi":"https://doi.org/10.1145/3698587.3701429"},"language":"en","primary_location":{"id":"doi:10.1145/3698587.3701429","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3698587.3701429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"},"type":"article","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/A5112326955","display_name":"Aaditya Shah","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116845","display_name":"Illinois Mathematics and Science Academy","ror":"https://ror.org/024xf4148","country_code":"US","type":"education","lineage":["https://openalex.org/I4210116845"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aaditya Shah","raw_affiliation_strings":["Illinois Mathematics and Science Academy, Aurora, USA"],"affiliations":[{"raw_affiliation_string":"Illinois Mathematics and Science Academy, Aurora, USA","institution_ids":["https://openalex.org/I4210116845"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041434311","display_name":"Sugyan M. Dixit","orcid":"https://orcid.org/0000-0002-6313-7974"},"institutions":[{"id":"https://openalex.org/I4210150566","display_name":"Medical Discovery Partners (United States)","ror":"https://ror.org/04sxjet04","country_code":"US","type":"company","lineage":["https://openalex.org/I4210150566"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sugyan Dixit","raw_affiliation_strings":["Discovery Partners Institute, Chicago, USA"],"affiliations":[{"raw_affiliation_string":"Discovery Partners Institute, Chicago, USA","institution_ids":["https://openalex.org/I4210150566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112326955"],"corresponding_institution_ids":["https://openalex.org/I4210116845"],"apc_list":null,"apc_paid":null,"fwci":0.2464,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60724322,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9063000082969666,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9063000082969666,"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":0.7037163376808167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5949249863624573},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5834413766860962},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.5469406843185425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3875671923160553},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.05069923400878906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7037163376808167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5949249863624573},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5834413766860962},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.5469406843185425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3875671923160553},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.05069923400878906},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3698587.3701429","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3698587.3701429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3046775127"],"abstract_inverted_index":{"Recent":[0],"breakthroughs":[1],"in":[2,123,173,241],"deep":[3,77],"learning,":[4],"such":[5,132],"as":[6,40,133],"AlphaFold,":[7],"have":[8],"revolutionized":[9],"protein":[10,60,218,236,244],"structure":[11,93,219],"prediction":[12,58,220],"by":[13],"achieving":[14],"accuracy":[15],"levels":[16],"on":[17,88],"par":[18],"with":[19,152],"experimental":[20],"methods,":[21],"albeit":[22],"at":[23],"significantly":[24],"reduced":[25],"computational":[26,57],"costs.":[27],"However,":[28],"these":[29,83],"models":[30],"fail":[31],"to":[32,81,194,203,212],"consider":[33],"the":[34,38,53,89,95,112,124,188,196,214,222],"inherent":[35],"dynamics":[36,178],"of":[37,59,111,225,235],"proteins,":[39,226],"in-vito":[41],"protiens":[42],"are":[43,63],"constantly":[44],"shifting":[45],"conformations.":[46],"Currently,":[47],"Molecular":[48],"Dynamics":[49],"(MD)":[50],"simulations":[51],"remain":[52],"industry":[54],"standard":[55],"for":[56,117,177,183,231],"ensembles,":[61],"they":[62],"extremely":[64],"computationally":[65],"intensive":[66],"and":[67,92,107,109,146,170,198,221,238,246],"time":[68],"consuming.":[69],"Thus,":[70],"we":[71,129],"designed":[72],"a":[73,153,228],"novel":[74],"low-cost":[75],"multi-modal":[76],"learning":[78],"pipeline,":[79],"DynPred,":[80],"address":[82],"limitations.":[84],"DynPred":[85],"uses":[86],"data":[87,182],"protein's":[90],"sequence":[91,127],"from":[94,187],"RCSB":[96,189],"Protein":[97,190],"Data":[98,191],"Bank":[99,192],"(PDB),":[100],"which":[101,158],"has":[102],"over":[103],"200,000":[104],"structures":[105],"available.":[106],"outputs":[108],"estimation":[110],"root-mean-square":[113],"fluctuation":[114],"(RMSF)":[115],"values":[116],"each":[118],"alpha":[119],"carbon":[120],"(C\u03b1)":[121],"molecule":[122],"protein.":[125],"Using":[126],"data,":[128],"leverage":[130],"techniques":[131],"Position-Specific":[134],"Scoring":[135],"Matrices":[136],"(PSSM),":[137],"vector":[138],"embeddings":[139],"through":[140,144,165],"Unirep,":[141],"One-Hot":[142],"Encoding":[143],"convolutional":[145,163],"dense":[147],"layers.":[148],"We":[149,180],"combine":[150],"this":[151],"Graph":[154],"Convolutional":[155],"Network":[156],"(GCN),":[157],"outperforms":[159],"traditional":[160],"two-or-three":[161],"dimensional":[162],"approaches":[164],"embedding":[166],"features":[167],"into":[168],"nodes":[169],"edges,":[171],"resulting":[172],"an":[174],"accurate":[175],"method":[176],"prediction.":[179],"extracted":[181],"150":[184],"human":[185],"proteins":[186],"(PDB)":[193],"train":[195],"model":[197],"employed":[199],"state-of-the-art":[200],"bioinformatics":[201],"tools":[202],"create":[204],"detailed":[205],"feature":[206],"embeddings.":[207],"The":[208],"proposed":[209],"approach":[210],"aims":[211],"bridge":[213],"gap":[215],"between":[216],"static":[217],"dynamic":[223],"nature":[224],"offering":[227],"new":[229],"pathway":[230],"enhancing":[232],"our":[233],"understanding":[234],"behavior":[237],"its":[239],"implications":[240],"drug":[242],"design,":[243],"engineering,":[245],"disease":[247],"mechanism":[248],"elucidation.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}