{"id":"https://openalex.org/W7131272323","doi":"https://doi.org/10.48550/arxiv.2602.18472","title":"Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling","display_name":"Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7131272323","doi":"https://doi.org/10.48550/arxiv.2602.18472"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.18472","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021367292","display_name":"Shunqi Liu","orcid":"https://orcid.org/0000-0002-4613-1714"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Shunqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113975476","display_name":"Han Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126703859","display_name":"Tong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tong","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":0.22840535,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.30550000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.30550000071525574,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.25049999356269836,"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/T10375","display_name":"Pharmacogenetics and Drug Metabolism","score":0.21739999949932098,"subfield":{"id":"https://openalex.org/subfields/3004","display_name":"Pharmacology"},"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/physiologically-based-pharmacokinetic-modelling","display_name":"Physiologically based pharmacokinetic modelling","score":0.7645000219345093},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5220999717712402},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42910000681877136},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40459999442100525},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3400000035762787},{"id":"https://openalex.org/keywords/biopharmaceutical","display_name":"Biopharmaceutical","score":0.3393000066280365},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.3386000096797943},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.3140000104904175}],"concepts":[{"id":"https://openalex.org/C185867374","wikidata":"https://www.wikidata.org/wiki/Q2562769","display_name":"Physiologically based pharmacokinetic modelling","level":3,"score":0.7645000219345093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7182000279426575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5486999750137329},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5220999717712402},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5157999992370605},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C183696295","wikidata":"https://www.wikidata.org/wiki/Q2487696","display_name":"Biochemical engineering","level":1,"score":0.3476000130176544},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.34459999203681946},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C2781047461","wikidata":"https://www.wikidata.org/wiki/Q679692","display_name":"Biopharmaceutical","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30880001187324524},{"id":"https://openalex.org/C64903051","wikidata":"https://www.wikidata.org/wiki/Q2198549","display_name":"Drug development","level":3,"score":0.28139999508857727},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2779652045","wikidata":"https://www.wikidata.org/wiki/Q507443","display_name":"Pharmaceutical industry","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.18472","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.18472","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.18472","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.18472","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.4125751852989197}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Physiologically":[0,89],"Based":[1],"Pharmacokinetic":[2],"(PBPK)":[3],"modeling":[4,86],"is":[5,30],"a":[6,14,57,84,94,99,113,150],"cornerstone":[7],"of":[8],"model-informed":[9],"drug":[10,19],"development":[11],"(MIDD),":[12],"providing":[13],"mechanistic":[15,66],"framework":[16,63,137],"to":[17,102,124,144,152],"predict":[18],"absorption,":[20],"distribution,":[21],"metabolism,":[22],"and":[23,47,68,109],"excretion":[24],"(ADME).":[25],"Despite":[26],"its":[27],"utility,":[28],"adoption":[29],"hindered":[31],"by":[32],"high":[33],"computational":[34],"costs":[35],"for":[36,43],"large-scale":[37],"simulations,":[38],"difficulty":[39],"in":[40,49],"parameter":[41],"identification":[42],"complex":[44],"biological":[45],"systems,":[46],"uncertainty":[48],"interspecies":[50],"extrapolation.":[51],"In":[52],"this":[53],"work,":[54],"we":[55],"propose":[56],"unified":[58],"Scientific":[59],"Machine":[60],"Learning":[61],"(SciML)":[62],"that":[64,97,135],"bridges":[65],"rigor":[67],"data-driven":[69],"flexibility.":[70],"We":[71],"introduce":[72],"three":[73],"contributions:":[74],"(1)":[75],"Foundation":[76],"PBPK":[77],"Transformers,":[78],"which":[79],"treat":[80],"pharmacokinetic":[81],"forecasting":[82],"as":[83],"sequence":[85],"task;":[87],"(2)":[88],"Constrained":[90],"Diffusion":[91],"Models":[92],"(PCDM),":[93],"generative":[95],"approach":[96],"uses":[98],"physics-informed":[100],"loss":[101],"synthesize":[103],"biologically":[104],"compliant":[105],"virtual":[106],"patient":[107],"populations;":[108],"(3)":[110],"Neural":[111,118,122],"Allometry,":[112],"hybrid":[114],"architecture":[115],"combining":[116],"Graph":[117],"Networks":[119],"(GNNs)":[120],"with":[121],"ODEs":[123],"learn":[125],"continuous":[126],"cross-species":[127],"scaling":[128],"laws.":[129],"Experiments":[130],"on":[131],"synthetic":[132],"datasets":[133],"show":[134],"the":[136],"reduces":[138],"physiological":[139],"violation":[140],"rates":[141],"from":[142],"2.00%":[143],"0.50%":[145],"under":[146],"constraints":[147],"while":[148],"offering":[149],"path":[151],"faster":[153],"simulation.":[154]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-02-25T00:00:00"}
