{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:17:38Z","timestamp":1783005458759,"version":"3.54.5"},"reference-count":20,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Bioorganic & Medicinal Chemistry"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bmc.2026.118731","type":"journal-article","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T18:08:49Z","timestamp":1781460529000},"page":"118731","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Computational hybrid approach of PBPK simulation and CGCNN models for human intravenous pharmacokinetics"],"prefix":"10.1016","volume":"140","author":[{"given":"Kiyoshi","family":"Hasegawa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuya","family":"Seki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.bmc.2026.118731_bb0005","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1038\/nrd2445","article-title":"The influence of drug-like concepts on decision-making in medicinal chemistry","volume":"6","author":"Leeson","year":"2007","journal-title":"Nat Rev Drug Discov"},{"issue":"1\u20133","key":"10.1016\/j.bmc.2026.118731_bb0010","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"46","author":"Lipinski","year":"2001","journal-title":"Adv Drug Deliv Rev"},{"issue":"9","key":"10.1016\/j.bmc.2026.118731_bb0015","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1021\/tx200211v","article-title":"Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety","volume":"24","author":"Meanwell","year":"2011","journal-title":"Chem Res Toxicol"},{"issue":"7","key":"10.1016\/j.bmc.2026.118731_bb0020","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1038\/nrd4609","article-title":"An analysis of the attrition of drug candidates from four major pharmaceutical companies","volume":"14","author":"Waring","year":"2015","journal-title":"Nat Rev Drug Discov"},{"issue":"3","key":"10.1016\/j.bmc.2026.118731_bb0025","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1038\/nrd1032","article-title":"ADMET in silico modelling: towards prediction paradise?","volume":"2","author":"van de Waterbeemd","year":"2003","journal-title":"Nat Rev Drug Discov"},{"issue":"14","key":"10.1016\/j.bmc.2026.118731_bb0030","doi-asserted-by":"crossref","first-page":"9697","DOI":"10.1021\/acs.jmedchem.3c00481","article-title":"DruMAP: a novel drug metabolism and pharmacokinetics analysis platform","volume":"66","author":"Kawashima","year":"2023","journal-title":"J Med Chem"},{"key":"10.1016\/j.bmc.2026.118731_bb0035","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1186\/s13321-025-01066-5","article-title":"PKSmart: an open-source computational model to predict intravenous pharmacokinetics of small molecules","volume":"17","author":"Seal","year":"2025","journal-title":"J Cheminformatics"},{"issue":"7","key":"10.1016\/j.bmc.2026.118731_bb0040","doi-asserted-by":"crossref","first-page":"7737","DOI":"10.1021\/acs.jmedchem.5c00340","article-title":"Application of machine learning and mechanistic modeling to predict intravenous pharmacokinetic profiles in humans","volume":"68","author":"Jia","year":"2025","journal-title":"J Med Chem"},{"issue":"7","key":"10.1016\/j.bmc.2026.118731_bb0045","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1124\/dmd.108.020479","article-title":"Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds","volume":"36","author":"Obach","year":"2008","journal-title":"Drug Metab Dispos"},{"issue":"3","key":"10.1016\/j.bmc.2026.118731_bb0050","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/jcc.24667","article-title":"Improving scoring-docking-screening powers of protein-ligand scoring functions using random Forest","volume":"38","author":"Wang","year":"2017","journal-title":"J Comput Chem"},{"issue":"11\u221212","key":"10.1016\/j.bmc.2026.118731_bb0055","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1002\/minf.201200090","article-title":"Evolution of PLS for modeling SAR and omics data","volume":"31","author":"Hasegawa","year":"2012","journal-title":"Molecular Inf"},{"key":"10.1016\/j.bmc.2026.118731_bb0060","first-page":"40","article-title":"Application of orthogonal L-shaped PLS to Chemogenomic data and its chemical interpretation from predictive and orthogonal latent variables","volume":"138","author":"Hasegawa","year":"2014","journal-title":"Chemom Intell Lab Syst"},{"key":"10.1016\/j.bmc.2026.118731_bb0065","unstructured":"Landrum, G. RDKit: Open-Source Cheminformatics Software. http:\/\/www.rdkit.org."},{"issue":"3","key":"10.1016\/j.bmc.2026.118731_bb0070","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1021\/accountsmr.1c00238","article-title":"Learning matter: materials design with machine learning and atomistic simulations","volume":"3","author":"Axelrod","year":"2022","journal-title":"Acc Mater Res"},{"key":"10.1016\/j.bmc.2026.118731_bb0075","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/s41524-023-01173-7","article-title":"A physics-informed Bayesian optimization approach for material design: application to NiTi shape memory alloys","volume":"9","author":"Khatamsaz","year":"2023","journal-title":"npj Comput Mater"},{"key":"10.1016\/j.bmc.2026.118731_bb0080","doi-asserted-by":"crossref","first-page":"5509","DOI":"10.1038\/s41467-024-49775-z","article-title":"Machine learning-enabled forward prediction and inverse design of 4D-printed active plates","volume":"15","author":"Sun","year":"2024","journal-title":"Nat Commun"},{"key":"10.1016\/j.bmc.2026.118731_bb0085","unstructured":"Hasegawa K., Prediction of Human Pharmacokinetics and Drug Design Using Machine Learning and Generative AI, Monthly PHARM STAGE, May 2026 Issue in press."},{"key":"10.1016\/j.bmc.2026.118731_bb0090","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2022.1029073","article-title":"Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan","volume":"13","author":"Grzegorzewski","year":"2022","journal-title":"Front Pharmacol"},{"key":"10.1016\/j.bmc.2026.118731_bb0095","unstructured":"DeepChem https:\/\/github.com\/deepchem\/deepchem."},{"key":"10.1016\/j.bmc.2026.118731_bb0100","unstructured":"PK-Sim12 https:\/\/github.com\/Open-Systems-Pharmacology\/PK-Sim."}],"container-title":["Bioorganic & Medicinal Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0968089626001872?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0968089626001872?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T14:54:49Z","timestamp":1783004089000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0968089626001872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":20,"alternative-id":["S0968089626001872"],"URL":"https:\/\/doi.org\/10.1016\/j.bmc.2026.118731","relation":{},"ISSN":["0968-0896"],"issn-type":[{"value":"0968-0896","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Computational hybrid approach of PBPK simulation and CGCNN models for human intravenous pharmacokinetics","name":"articletitle","label":"Article Title"},{"value":"Bioorganic & Medicinal Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bmc.2026.118731","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"118731"}}