{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:34:12Z","timestamp":1793126052944,"version":"4.5.0"},"reference-count":201,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Pharmaceutical Sciences"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ejps.2026.107688","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:45:29Z","timestamp":1791960329000},"page":"107688","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Artificial intelligence and machine learning, a game changer in pharmaceutical development? \u2013 An updated perspective arising from Journees Galeniques 2024"],"prefix":"10.1016","volume":"227","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4894-7765","authenticated-orcid":false,"given":"Ren\u00e9","family":"Holm","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Konagurthu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tausif","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillaume","family":"Louit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5433-8398","authenticated-orcid":false,"given":"Brendan","family":"Griffen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sune H.","family":"Holm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phatsawee","family":"Jansook","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0164-0814","authenticated-orcid":false,"given":"Wei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9655-7510","authenticated-orcid":false,"given":"Yi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Allen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiota","family":"Angeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christel A.S.","family":"Bergstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anette","family":"M\u00fcllertz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4789-7380","authenticated-orcid":false,"given":"Daniel","family":"Reker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferran","family":"Sanz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2963-2645","authenticated-orcid":false,"given":"Martin","family":"Kuentz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejps.2026.107688_bib0001","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.ejpb.2020.05.008","article-title":"Six years of progress in the oral biopharmaceutics area \u2013 a summary from the IMI OrBiTo project","volume":"152","author":"Abrahamsson","year":"2020","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0002","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1021\/acs.jcim.1c01580","article-title":"Emerging landscape of computational modeling in pharmaceutical development","volume":"62","author":"Abramov","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0003","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-72629-z","article-title":"Machine learning- a new paradigm in nanoparticle-mediated drug delivery to cancerous tissues through the human cardiovascular system enhanced by magnetic field","volume":"14","author":"Akhtar","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejps.2026.107688_bib0004","series-title":"arXiv preprint","article-title":"The history began from AlexNet: a comprehensive survey on deep learning approaches","author":"Alom","year":"2018"},{"key":"10.1016\/j.ejps.2026.107688_bib0005","doi-asserted-by":"crossref","first-page":"4661","DOI":"10.1021\/acs.molpharmaceut.9b00801","article-title":"From quantum chemistry to prediction of drug solubility in glycerides","volume":"16","author":"Alsenz","year":"2019","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0006","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1021\/acs.molpharmaceut.5b00704","article-title":"Tools for early prediction of drug loading in lipid-based formulations","volume":"13","author":"Alsk\u00e4r","year":"2016","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0007","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","article-title":"Low data drug discovery with one-shot learning","volume":"3","author":"Altae-Tran","year":"2017","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0008","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1021\/ci5004834","article-title":"Experimental and computational prediction of glass transition temperature of drugs","volume":"54","author":"Alzghoul","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0009","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"10.1016\/j.ejps.2026.107688_bib0010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000016","article-title":"To explain or not to explain?\u2014Artificial intelligence explainability in clinical decision support systems","volume":"1","author":"Amann","year":"2022","journal-title":"PLOS Digit. Health"},{"key":"10.1016\/j.ejps.2026.107688_bib0012","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5599\/admet.766","article-title":"Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database","volume":"8","author":"Avdeef","year":"2020","journal-title":"ADMET DMPK"},{"key":"10.1016\/j.ejps.2026.107688_bib0011","series-title":"Predicting Solubility Of New Drugs: Handbook Of Critically Curated Data For Pharmaceutical Research","author":"Avdeef","year":"2024"},{"key":"10.1016\/j.ejps.2026.107688_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2021.05.016","article-title":"Machine learning directed drug formulation development","volume":"175","author":"Bannigan","year":"2021","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0014","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/s41467-022-35343-w","article-title":"Machine learning models to accelerate the design of polymeric long-acting injectables","volume":"14","author":"Bannigan","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejps.2026.107688_bib0015","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1186\/s13321-024-00911-3","article-title":"Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning","volume":"16","author":"Bao","year":"2024","journal-title":"J. Cheminformatics"},{"key":"10.1016\/j.ejps.2026.107688_bib0016","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.tips.2019.07.005","article-title":"Artificial Intelligence for Drug Toxicity and Safety","volume":"40","author":"Basile","year":"2019","journal-title":"Trends Pharmacol. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0017","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.ejps.2026.107688_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejps.2021.106018","article-title":"Machine learning methods for prediction of food effects on bioavailability: a comparison of support vector machines and artificial neural networks","volume":"168","author":"Bennett-Lenane","year":"2022","journal-title":"Eur. J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0019","doi-asserted-by":"crossref","first-page":"3248","DOI":"10.1016\/j.xphs.2020.08.008","article-title":"A retrospective biopharmaceutical analysis of >800 approved oral drug products: are drug properties of solid dispersions and lipid-based formulations distinctive?","volume":"109","author":"Bennett-Lenane","year":"2020","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0020","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.11261","article-title":"Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation","volume":"9","author":"Bepari","year":"2021","journal-title":"PeerJ"},{"key":"10.1016\/j.ejps.2026.107688_bib0021","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.addr.2016.02.005","article-title":"Computational prediction of formulation strategies for beyond-rule-of-5 compounds","volume":"101","author":"Bergstr\u00f6m","year":"2016","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0022","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1016\/j.xphs.2022.09.003","article-title":"Utility of physiologically based biopharmaceutics modeling (PBBM) in regulatory perspective: application to supersede f2, enabling biowaivers & creation of dissolution safe space","volume":"111","author":"Bhattiprolu","year":"2022","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0023","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ejpb.2019.10.012","article-title":"Solubility of pharmaceutical ingredients in triglycerides","volume":"145","author":"Brinkmann","year":"2019","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0024","doi-asserted-by":"crossref","DOI":"10.1002\/minf.202100321","article-title":"Benchmarking accuracy and generalizability of four graph neural networks using large in vitro ADME datasets from different chemical spaces","volume":"41","author":"Broccatelli","year":"2022","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejps.2026.107688_bib0025","series-title":"Proceedings of the 1st Conference on Fairness, Accountability and Transparency. Presented at the Conference on Fairness, Accountability and Transparency","first-page":"77","article-title":"Gender shades: intersectional accuracy disparities in commercial gender classification","author":"Buolamwini","year":"2018"},{"key":"10.1016\/j.ejps.2026.107688_bib0026","doi-asserted-by":"crossref","first-page":"4940","DOI":"10.1002\/jps.22217","article-title":"The developability classification system: application of biopharmaceutics concepts to formulation development","volume":"99","author":"Butler","year":"2010","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0027","article-title":"Near-infrared fluorophores with absolute aggregation-caused quenching and negligible fluorescence re-illumination for in vivo bioimaging of nanocarriers","volume":"4","author":"Cai","year":"2023","journal-title":"Aggreg. Hoboken"},{"key":"10.1016\/j.ejps.2026.107688_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2023.103516","article-title":"New avenues in artificial-intelligence-assisted drug discovery","volume":"28","author":"Cerchia","year":"2023","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejps.2026.107688_bib0029","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejps.2026.107688_bib0030","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10915-022-02059-4","article-title":"Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models","volume":"94","author":"Cheng","year":"2023","journal-title":"J. Sci. Computing"},{"key":"10.1016\/j.ejps.2026.107688_bib0031","doi-asserted-by":"crossref","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"10.1016\/j.ejps.2026.107688_bib0032","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/1750-1172-3-11","article-title":"Adaptive design methods in clinical trials - a review","volume":"3","author":"Chow","year":"2008","journal-title":"Orphanet J. Rare Dis."},{"key":"10.1016\/j.ejps.2026.107688_bib0033","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ces.2018.01.010","article-title":"Computational fluid dynamic studies of mixers for highly viscous shear thinning fluids and PIV validation","volume":"79","author":"Cortada-Garcia","year":"2018","journal-title":"CES"},{"key":"10.1016\/j.ejps.2026.107688_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.yrtph.2022.105249","article-title":"A scheme to evaluate structural alerts to predict toxicity - assessing confidence by characterising uncertainties","volume":"135","author":"Cronin","year":"2022","journal-title":"Regul. Toxicol. Pharmacol. RTP"},{"key":"10.1016\/j.ejps.2026.107688_bib0035","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1002\/jps.21246","article-title":"Evaluation of the impact of surfactant digestion on the bioavailability of danazol after oral administration of lipidic self-emulsifying formulations to dogs","volume":"97","author":"Cuin\u00e9","year":"2008","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0036","doi-asserted-by":"crossref","first-page":"7022","DOI":"10.1021\/acs.molpharmaceut.5c01065","article-title":"Machine learning modeling for ABC transporter efflux and inhibition: data curation, model development, and new compound interaction predictions","volume":"22","author":"Daood","year":"2025","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0037","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1007\/s10462-021-10058-4","article-title":"Machine learning in drug discovery: a review","volume":"55","author":"Dara","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0038","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1037\/xge0000033","article-title":"Algorithm aversion: people erroneously avoid algorithms after seeing them err","volume":"144","author":"Dietvorst","year":"2015","journal-title":"J. Exp. Psychol. Gen."},{"key":"10.1016\/j.ejps.2026.107688_bib0039","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","article-title":"Innovation in the pharmaceutical industry: New estimates of R&D costs","volume":"47","author":"DiMasi","year":"2016","journal-title":"J. Health Econ."},{"key":"10.1016\/j.ejps.2026.107688_bib0040","series-title":"arXiv preprint","article-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017"},{"key":"10.1016\/j.ejps.2026.107688_bib0041","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.tips.2023.11.001","article-title":"Computational and artificial intelligence-based approaches for drug metabolism and transport prediction","volume":"45","author":"Dudas","year":"2024","journal-title":"Trends Pharmacol. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0042","article-title":"Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations","volume":"200","author":"Ejskj\u00e6r","year":"2024","journal-title":"Eur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0043","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/s41563-019-0338-z","article-title":"Exploiting machine learning for end-to-end drug discovery and development","volume":"18","author":"Ekins","year":"2019","journal-title":"Nat. Mater."},{"key":"10.1016\/j.ejps.2026.107688_bib0044","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1021\/acs.cgd.0c01730","article-title":"Crystallization kinetics in fasted-state simulated and aspirated human intestinal fluids","volume":"21","author":"Elkhabaz","year":"2021","journal-title":"Cryst. Growth Des."},{"key":"10.1016\/j.ejps.2026.107688_bib0045","series-title":"Reflection Paper on the Use of Artificial Intelligence (AI) in the Medicinal Product Lifecycle","year":"2024"},{"key":"10.1016\/j.ejps.2026.107688_bib0048","unstructured":"EPO, 2026. European patent office. Inventorship. https:\/\/www.epo.org\/en\/news-events\/in-focus\/digital-innovations\/inventorship (accessed 22 September 2026)."},{"key":"10.1016\/j.ejps.2026.107688_bib0046","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1007\/s11095-014-1487-z","article-title":"Computational prediction of drug solubility in fasted simulated and aspirated human intestinal fluid","volume":"32","author":"Fagerberg","year":"2015","journal-title":"Pharm. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0047","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.3390\/pharmaceutics14101998","article-title":"Reliable prediction of caco-2 permeability by supervised recursive machine learning approaches","volume":"14","author":"Falc\u00f3n-Cano","year":"2022","journal-title":"Pharmaceutics"},{"key":"10.1016\/j.ejps.2026.107688_bib0052","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.1021\/acs.jcim.3c00160","article-title":"Prospective validation of machine learning algorithms for absorption, distribution, metabolism, and excretion prediction: an industrial perspective","volume":"63","author":"Fang","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0049","unstructured":"FDA. (2021). Artificial intelligence\/machine learning (AI\/ML)-enabled medical devices. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices (accessed 21 April 2025)."},{"key":"10.1016\/j.ejps.2026.107688_bib0050","unstructured":"FDA 2025. Considerations for the use of artificial intelligence to support regulatory decision-making for drug and biological products guidance for industry and other interested parties. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological (accessed 21 April 2025)."},{"key":"10.1016\/j.ejps.2026.107688_bib0051","series-title":"Guiding Principles of Good AI Practice in Drug Development","year":"2026"},{"key":"10.1016\/j.ejps.2026.107688_bib0053","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.addr.2016.04.007","article-title":"50years of oral lipid-based formulations: provenance, progress and future perspectives","volume":"101","author":"Feeney","year":"2016","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0054","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1186\/s12967-017-1285-6","article-title":"In silico prediction of novel therapeutic targets using gene\u2013disease association data","volume":"15","author":"Ferrero","year":"2017","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0055","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ejpb.2019.05.020","article-title":"Potential for pharmaceutical excipients to impact absorption: a mechanistic review for BCS Class 1 and 3 drugs","volume":"141","author":"Flanagan","year":"2019","journal-title":"Eur. J. Pharm. Biopharm. Off. J. Arbeitsgemeinschaft Pharm. Verfahrenstechnik EV"},{"key":"10.1016\/j.ejps.2026.107688_bib0056","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1021\/acs.chemrestox.3c00305","article-title":"Deep learning models compared to experimental variability for the prediction of CYP3A4 time-dependent inhibition","volume":"37","author":"Fluetsch","year":"2024","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejps.2026.107688_bib0057","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s13321-023-00769-x","article-title":"DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning","volume":"15","author":"Fralish","year":"2023","journal-title":"J. Cheminformatics"},{"key":"10.1016\/j.ejps.2026.107688_bib0058","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1038\/s43588-024-00704-6","article-title":"Taking a deep dive with active learning for drug discovery","volume":"4","author":"Fralish","year":"2024","journal-title":"Nat. Comput. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0059","doi-asserted-by":"crossref","DOI":"10.3389\/fddsv.2026.1859068","article-title":"Pairwise learning for molecular property prediction and optimization","volume":"6","author":"Fralish","year":"2026","journal-title":"Front. Drug Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0060","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.1039\/D4MD00325J","article-title":"Leveraging bounded datapoints to classify molecular potency improvements","volume":"15","author":"Fralish","year":"2024","journal-title":"RSC Med. Chem."},{"key":"10.1016\/j.ejps.2026.107688_bib0061","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1007\/s11030-021-10266-8","article-title":"AI in drug development: a multidisciplinary perspective","volume":"25","author":"Gallego","year":"2021","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejps.2026.107688_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpharm.2021.121329","article-title":"Machine learning predicts the effect of food on orally administered medicines","volume":"611","author":"Gavins","year":"2022","journal-title":"Int. J. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0063","article-title":"A machine learning approach for the prediction of aqueous solubility of pharmaceuticals: a comparative model and dataset analysis","volume":"3","author":"Ghanavati","year":"2024","journal-title":"Digit. Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0064","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/17460441.2024.2403639","article-title":"Data-centric challenges with the application and adoption of artificial intelligence for drug discovery","volume":"19","author":"Ghislat","year":"2024","journal-title":"Expert Opin. Drug Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108702","article-title":"Advances in artificial intelligence for drug delivery and development: A comprehensive review","volume":"178","author":"Gholap","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0066","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s13321-019-0391-2","article-title":"Dataset\u2019s chemical diversity limits the generalizability of machine learning predictions","volume":"11","author":"Glavatskikh","year":"2019","journal-title":"J. Cheminformatics"},{"key":"10.1016\/j.ejps.2026.107688_bib0067","volume":"1","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.ejps.2026.107688_bib0068","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jconrel.2024.06.045","article-title":"Machine learning in drug delivery","volume":"373","author":"Gormley","year":"2024","journal-title":"J. Control. Release Off. J. Control. Release Soc."},{"key":"10.1016\/j.ejps.2026.107688_bib0069","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1021\/acs.cgd.2c01072","article-title":"Exploring the cocrystal landscape of posaconazole by combining high-throughput screening experimentation with computational chemistry","volume":"23","author":"Guidetti","year":"2023","journal-title":"Cryst. Growth Des."},{"key":"10.1016\/j.ejps.2026.107688_bib0070","doi-asserted-by":"crossref","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D., Giannotti, F., 2018. A survey of methods for explaining black box models. arXiv.org.","DOI":"10.1145\/3236009"},{"key":"10.1016\/j.ejps.2026.107688_bib0071","first-page":"1952","article-title":"Review of machine learning and deep learning models for toxicity prediction","volume":"248","author":"Guo","year":"2023","journal-title":"Exp. Biol. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0072","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejps.2024.106946","article-title":"The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation","volume":"204","author":"Han","year":"2025","journal-title":"Eur. J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0074","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2025.104422","article-title":"Computational approaches to DMPK: a realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties","volume":"30","author":"Handa","year":"2025","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejps.2026.107688_bib0073","doi-asserted-by":"crossref","first-page":"3060","DOI":"10.1021\/acs.molpharmaceut.3c00071","article-title":"Prediction of compound plasma concentration-time profiles in mice using random forest","volume":"20","author":"Handa","year":"2023","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0075","doi-asserted-by":"crossref","DOI":"10.1002\/agt2.163","article-title":"Accurate and sensitive probing of onset of micellization based on absolute aggregation-caused quenching effect","volume":"3","author":"He","year":"2022","journal-title":"Aggregate"},{"key":"10.1016\/j.ejps.2026.107688_bib0076","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1021\/acs.jcim.3c00799","article-title":"MELLODDY: Cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information","volume":"64","author":"Heyndrickx","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0077","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.addr.2019.01.009","article-title":"Bridging the gaps between academic research and industrial product developments of lipid-based formulations","volume":"142","author":"Holm","year":"2019","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0078","first-page":"1","article-title":"On the justified use of ai decision support in evidence-based medicine: validity, explainability, and responsibility","author":"Holm","year":"2023","journal-title":"Camb. Q. Healthc. Ethics"},{"key":"10.1016\/j.ejps.2026.107688_bib0079","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ejpb.2019.02.007","article-title":"Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning","volume":"137","author":"Hossain","year":"2019","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0080","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1016\/j.nano.2015.06.013","article-title":"Environment-responsive aza-BODIPY dyes quenching in water as potential probes to visualize the in vivo fate of lipid-based nanocarriers","volume":"11","author":"Hu","year":"2015","journal-title":"Nanomedicine Nanotechnol. Biol. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0081","series-title":"ICH M15: General Principles for Model-Informed Drug Development","article-title":"International council for harmonisation of technical requirements for pharmaceuticals for human use (ICH). (2026)","year":"2026"},{"key":"10.1016\/j.ejps.2026.107688_bib0082","doi-asserted-by":"crossref","first-page":"2976","DOI":"10.1021\/acs.molpharmaceut.4c01431","article-title":"Machine learning prediction and validation of plasma concentration-time profiles","volume":"22","author":"Iwata","year":"2025","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0083","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/6679512","article-title":"Involvement of machine learning tools in healthcare decision making","author":"Jayatilake","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"10.1016\/j.ejps.2026.107688_bib0084","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."},{"key":"10.1016\/j.ejps.2026.107688_bib0085","doi-asserted-by":"crossref","first-page":"17690","DOI":"10.1021\/acs.est.3c00653","article-title":"Advancing computational toxicology by interpretable machine learning","volume":"57","author":"Jia","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.ejps.2026.107688_bib0086","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.ejps.2026.107688_bib0087","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2024.103879","article-title":"Data standards in drug discovery: a long way to go","volume":"29","author":"Kelm","year":"2024","journal-title":"Drug Discov. Today."},{"key":"10.1016\/j.ejps.2026.107688_bib0088","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s11095-024-03688-0","article-title":"The use of global sensitivity analysis to assess the oral absorption of weakly basic compounds: a case example of dipyridamole","volume":"41","author":"Kesharwani","year":"2024","journal-title":"Pharm. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0089","doi-asserted-by":"crossref","unstructured":"Khuat, T.T., Bassett, R., Otte, E., Grevis-James, A., Gabrys, B., 2023. Applications of machine learning in biopharmaceutical process development and manufacturing: current trends, challenges, and opportunities. arXiv:2310.09991. https:\/\/doi.org\/10.48550\/arXiv.2310.09991.","DOI":"10.1016\/j.compchemeng.2024.108585"},{"key":"10.1016\/j.ejps.2026.107688_bib0090","first-page":"109","article-title":"Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection","volume":"87","author":"Kim","year":"2017","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"10.1016\/j.ejps.2026.107688_bib0091","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.jpba.2016.08.028","article-title":"Miniaturized X-ray powder diffraction assay (MixRay) for quantitative kinetic analysis of solvent-mediated phase transformations in pharmaceutics","volume":"131","author":"Kirchmeyer","year":"2016","journal-title":"J. Pharm. Biomed. Anal."},{"key":"10.1016\/j.ejps.2026.107688_bib0093","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2026.115784","article-title":"The predictive edge: modeling and simulation in drug product development","volume":"230","author":"Konagurthu","year":"2026","journal-title":"Adv Drug Deliv Rev"},{"key":"10.1016\/j.ejps.2026.107688_bib0092","unstructured":"Konagurthu, Sanjay, Reynolds, Tom. Strategies for API solubility and bioavailability enhancement. https:\/\/www.patheon.com\/us\/en\/insights-resources\/whitepapers\/strategies-for-api-solubility-and-bioavailability-enhancement.html (accessed 30 June 2026)."},{"key":"10.1016\/j.ejps.2026.107688_bib0094","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.xphs.2020.10.068","article-title":"Synergistic computational modeling approaches as team players in the game of solubility predictions","volume":"110","author":"Kuentz","year":"2021","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0095","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1016\/j.xphs.2021.02.004","article-title":"Rational selection of bio-enabling oral drug formulations - a PEARRL commentary","volume":"110","author":"Kuentz","year":"2021","journal-title":"J Pharm Sci"},{"key":"10.1016\/j.ejps.2026.107688_bib0096","first-page":"554","article-title":"In silico prediction of the solubility advantage for amorphous drugs - Are there property-based rules for drug discovery and early pharmaceutical development?","volume":"48","author":"Kuentz","year":"2013","journal-title":"Eur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0097","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1016\/j.ejpb.2013.09.013","article-title":"Model-based analysis of high shear wet granulation from batch to continuous processes in pharmaceutical production\u2013a critical review","volume":"85","author":"Kumar","year":"2013","journal-title":"Eur. J. Pharm. Biopharm. Off. J. Arbeitsgemeinschaft Pharm. Verfahrenstechnik EV"},{"key":"10.1016\/j.ejps.2026.107688_bib0098","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cmpb.2016.11.006","article-title":"Biopharmaceutics data management system for anonymised data sharing and curation: first application with OrBiTo IMI project","volume":"140","author":"Lacy-Jones","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.ejps.2026.107688_bib0099","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-11823-z","article-title":"Machine learning analysis of drug solubility via green approach to enhance drug solubility for poor soluble medications in continuous manufacturing","volume":"15","author":"Lahiq","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejps.2026.107688_bib0100","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1021\/acs.molpharmaceut.4c00080","article-title":"Comparative analysis of chemical descriptors by machine learning reveals atomistic insights into solute-lipid interactions","volume":"21","author":"Lange","year":"2024","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0101","first-page":"339","article-title":"Oral bioavailability of cinnarizine in dogs: relation to SNEDDS droplet size, drug solubility and in vitro precipitation","volume":"48","author":"Larsen","year":"2013","journal-title":"Eur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0102","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3389\/fmed.2019.00275","article-title":"The innovative medicines initiative -10 years of public-private collaboration","volume":"6","author":"Laverty","year":"2019","journal-title":"Front. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0103","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.ejps.2026.107688_bib0104","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s41524-023-01012-9","article-title":"A critical examination of robustness and generalizability of machine learning prediction of materials properties","volume":"9","author":"Li","year":"2023","journal-title":"Npj Comput. Mater."},{"key":"10.1016\/j.ejps.2026.107688_bib0106","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2022-071594","article-title":"Comparative risk of thrombosis with thrombocytopenia syndrome or thromboembolic events associated with different covid-19 vaccines: international network cohort study from five European countries and the US","volume":"379","author":"Li","year":"2022","journal-title":"BMJ"},{"key":"10.1016\/j.ejps.2026.107688_bib0107","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s11095-024-03725-y","article-title":"A combination of machine learning and PBPK modeling approach for pharmacokinetics prediction of small molecules in humans","volume":"41","author":"Li","year":"2024","journal-title":"Pharm. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0108","article-title":"Yoked learning in molecular data science","volume":"5","author":"Li","year":"2024","journal-title":"Artif. Intell. Life Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0109","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclinepi.2024.111612","article-title":"Real-time adaptive randomization of clinical trials","volume":"178","author":"Liberali","year":"2025","journal-title":"J. Clin. Epidemiol."},{"key":"10.1016\/j.ejps.2026.107688_bib0110","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."},{"key":"10.1016\/j.ejps.2026.107688_bib0111","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1002\/cpt.1771","article-title":"Application of Machine Learning in Drug Development and Regulation: Current Status and Future Potential","volume":"107","author":"Liu","year":"2020","journal-title":"Clin. Pharmacol. Ther."},{"key":"10.1016\/j.ejps.2026.107688_bib0112","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1016\/j.drudis.2018.05.010","article-title":"Machine learning in chemoinformatics and drug discovery","volume":"23","author":"Lo","year":"2018","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejps.2026.107688_bib0113","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1200\/JCO.2024.42.16_suppl.1601","article-title":"Racial\/ethnic representation and disparities in preclinical cancer models","volume":"42","author":"Loree","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"10.1016\/j.ejps.2026.107688_bib0114","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1007\/s12272-015-0640-5","article-title":"Role of computer-aided drug design in modern drug discovery","volume":"38","author":"Macalino","year":"2015","journal-title":"Arch. Pharm. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0115","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1038\/s41467-019-12928-6","article-title":"A Bayesian machine learning approach for drug target identification using diverse data types","volume":"10","author":"Madhukar","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejps.2026.107688_bib0116","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpharm.2021.121146","article-title":"Use of machine learning in prediction of granule particle size distribution and tablet tensile strength in commercial pharmaceutical manufacturing","volume":"609","author":"M\u00e4ki-Lohiluoma","year":"2021","journal-title":"Int. J. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0117","doi-asserted-by":"crossref","first-page":"205","DOI":"10.21474\/IJAR01\/19245","article-title":"Integration of artificial intelligence in adaptive trial designs: enhancing efficiency and patient-centric outcomes","volume":"12","author":"Makutam","year":"2024","journal-title":"Int. J. Adv. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0118","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/17460441.2024.2376643","article-title":"Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery","volume":"19","author":"Manen-Freixa","year":"2024","journal-title":"Expert Opin. Drug Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0119","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s43588-024-00594-8","article-title":"Characterizing emerging companies in computational drug development","volume":"4","author":"Markey","year":"2024","journal-title":"Nat. Comput. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0120","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1093\/bib\/bbz026","article-title":"Validation strategies for target prediction methods","volume":"21","author":"Mathai","year":"2020","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.ejps.2026.107688_bib0121","doi-asserted-by":"crossref","first-page":"844","DOI":"10.3390\/pharmaceutics12090844","article-title":"Bottom-up physiologically based oral absorption modeling of free weak base drugs","volume":"12","author":"Matsumura","year":"2020","journal-title":"Pharmaceutics"},{"key":"10.1016\/j.ejps.2026.107688_bib0122","doi-asserted-by":"crossref","DOI":"10.3389\/fenvs.2015.00080","article-title":"DeepTox: toxicity prediction using deep learning","volume":"3","author":"Mayr","year":"2016","journal-title":"Front. Environ. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0123","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.1039\/C8SC00148K","article-title":"Large-scale comparison of machine learning methods for drug target prediction on ChEMBL","volume":"9","author":"Mayr","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0124","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.ejpb.2014.08.006","article-title":"In vitro-in vivo evaluation of lipid based formulations of the CETP inhibitors CP-529,414 (torcetrapib) and CP-532,623","volume":"88","author":"McEvoy","year":"2014","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0125","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.drudis.2018.11.018","article-title":"Computational modeling for formulation design","volume":"24","author":"Mehta","year":"2019","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejps.2026.107688_bib0126","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1038\/s41565-024-01673-7","article-title":"A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research","volume":"19","author":"Mendes","year":"2024","journal-title":"Nat. Nanotechnol."},{"key":"10.1016\/j.ejps.2026.107688_bib0128","doi-asserted-by":"crossref","DOI":"10.1063\/5.0038602","article-title":"Viscoelastic flow instabilities in static mixers: onset and effect on the mixing efficiency","volume":"33","author":"Migliozzi","year":"2021","journal-title":"Physics of Fluids"},{"key":"10.1016\/j.ejps.2026.107688_bib0127","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2174\/0127724328311400240823062829","article-title":"Leveraging generative AI for drug safety and pharmacovigilance","volume":"20","author":"Mishra","year":"2025","journal-title":"Curr. Rev. Clin. Exp. Pharmacol."},{"key":"10.1016\/j.ejps.2026.107688_bib0129","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.3390\/ijms22041676","article-title":"Advances in de novo drug design: from conventional to machine learning methods","volume":"22","author":"Mouchlis","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0130","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1021\/acs.molpharmaceut.4c00991","article-title":"Establishing a pharmacoinformatics repository of approved medicines: a database to support drug product development","volume":"22","author":"Murray","year":"2025","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0131","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejps.2023.106562","article-title":"Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation","volume":"191","author":"Murray","year":"2023","journal-title":"Eur. J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0132","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1021\/op300241f","article-title":"Specialized solid form screening techniques","volume":"17","author":"Newman","year":"2013","journal-title":"Org. Process Res. Dev."},{"key":"10.1016\/j.ejps.2026.107688_bib0133","article-title":"Pharmaceutical approaches for enhancing solubility and oral bioavailability of poorly soluble drugs","volume":"204","author":"Nyamba","year":"2024","journal-title":"Eur. J. Pharm. Biopharm. Off. J. Arbeitsgemeinschaft Pharm. Verfahrenstechnik EV"},{"key":"10.1016\/j.ejps.2026.107688_bib0134","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-05535-7","article-title":"Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations","volume":"15","author":"Obaidullah","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejps.2026.107688_bib0135","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1007\/s11095-012-0767-8","article-title":"Impact of excipient interactions on drug bioavailability from solid dosage forms","volume":"29","author":"Panakanti","year":"2012","journal-title":"Pharm. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0136","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1208\/s12249-025-03051-5","article-title":"Machine learning-based prediction of drug solubility in lipidic environments: the Sol_ME tool for optimizing lipid-based formulations with a preliminary apalutamide case study","volume":"26","author":"Patel","year":"2025","journal-title":"AAPS PharmSciTech"},{"key":"10.1016\/j.ejps.2026.107688_bib0137","first-page":"24","article-title":"ML and AI implementation insights for bio\/pharma manufacturing","volume":"36","author":"Pazhayattil","year":"2023","journal-title":"BioPharm International"},{"key":"10.1016\/j.ejps.2026.107688_bib0138","doi-asserted-by":"crossref","first-page":"3225","DOI":"10.1007\/s11095-013-1083-7","article-title":"Computational prediction of drug solubility in lipid based formulation excipients","volume":"30","author":"Persson","year":"2013","journal-title":"Pharm. Res."},{"issue":"D1","key":"10.1016\/j.ejps.2026.107688_bib0139","first-page":"D845","article-title":"The DisGeNET knowledge platform for disease genomics: 2019 update","volume":"48","author":"Pi\u00f1ero","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"10.1016\/j.ejps.2026.107688_bib0140","doi-asserted-by":"crossref","first-page":"eaap7885","DOI":"10.1126\/sciadv.aap7885","article-title":"Deep reinforcement learning for de novo drug design","volume":"4","author":"Popova","year":"2018","journal-title":"Sci. Adv."},{"key":"10.1016\/j.ejps.2026.107688_bib0141","first-page":"36","article-title":"Machine learning for knowledge extraction from PHR big data","volume":"202","author":"Poulymenopoulou","year":"2014","journal-title":"Stud. Health Technol. Inform."},{"key":"10.1016\/j.ejps.2026.107688_bib0142","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.addr.2019.05.009","article-title":"Towards more accurate bioimaging of drug nanocarriers: turning aggregation-caused quenching into a useful tool","volume":"143","author":"Qi","year":"2019","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0143","doi-asserted-by":"crossref","first-page":"903","DOI":"10.3390\/molecules29040903","article-title":"Machine learning empowering drug discovery: applications, opportunities and challenges","volume":"29","author":"Qi","year":"2024","journal-title":"Molecules"},{"key":"10.1016\/j.ejps.2026.107688_bib0144","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2020.02.094","article-title":"Machine learning uncovers food- and excipient-drug interactions","volume":"30","author":"Reker","year":"2020","journal-title":"Cell Rep"},{"key":"10.1016\/j.ejps.2026.107688_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejps.2023.106505","article-title":"Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: an InPharma commentary","volume":"188","author":"Reppas","year":"2023","journal-title":"Eur. J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0146","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1021\/acs.molpharmaceut.2c00680","article-title":"Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses","volume":"20","author":"Rodr\u00edguez-P\u00e9rez","year":"2023","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0147","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2026.115781","article-title":"Artificial intelligence and machine learning guided optimization in drug delivery","volume":"232","author":"Ros","year":"2026","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0148","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1016\/j.xphs.2018.03.030","article-title":"A refined developability classification system","volume":"107","author":"Rosenberger","year":"2018","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0149","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2023.114821","article-title":"Recent progress in NIR-II fluorescence imaging-guided drug delivery for cancer theranostics","volume":"197","author":"Roy","year":"2023","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.xphs.2026.104262","article-title":"A computationally informed framework for lipid-based formulation developability: integrating predictive models with case studies","volume":"115","author":"Ryan","year":"2026","journal-title":"J Pharm Sci"},{"key":"10.1016\/j.ejps.2026.107688_bib0151","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.2174\/1386207325666211207153943","article-title":"Artificial intelligence (AI) in drugs and pharmaceuticals","volume":"25","author":"Sahu","year":"2022","journal-title":"Comb. Chem. High Throughput Screen."},{"key":"10.1016\/j.ejps.2026.107688_bib0152","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s40290-022-00441-z","article-title":"The use of artificial intelligence in pharmacovigilance: a systematic review of the literature","volume":"36","author":"Salas","year":"2022","journal-title":"Pharm. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0153","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2023.1060076","article-title":"Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation","volume":"10","author":"Samad","year":"2023","journal-title":"Front. Mol. Biosci."},{"key":"10.1016\/j.ejps.2026.107688_bib0154","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1146\/annurev-biodatasci-103123-095506","article-title":"Integrative data science in drug safety research: experiences, challenges, and perspectives","volume":"8","author":"Sanz","year":"2025","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0155","article-title":"Transitional initiatives for advancing the phasing out of the use of animals for drug and chemical safety testing: The IHI VICT3R project for reducing the use of animals by implementing virtual control groups","volume":"22","author":"Sanz","year":"2025","journal-title":"NAM J."},{"key":"10.1016\/j.ejps.2026.107688_bib0156","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1038\/d41573-023-00099-5","article-title":"eTRANSAFE: data science to empower translational safety assessment","volume":"22","author":"Sanz","year":"2023","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0157","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1038\/nrd.2017.177","article-title":"Legacy data sharing to improve drug safety assessment: the eTOX project","volume":"16","author":"Sanz","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0158","doi-asserted-by":"crossref","first-page":"2960","DOI":"10.1021\/acs.jcim.3c00322","article-title":"A small step toward generalizability: training a machine learning scoring function for structure-based virtual screening","volume":"63","author":"Scantlebury","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0159","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1021\/acs.jmedchem.5b01849","article-title":"De Novo design at the edge of chaos","volume":"59","author":"Schneider","year":"2016","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejps.2026.107688_bib0160","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","article-title":"Generating focused molecule libraries for drug discovery with recurrent neural networks","volume":"4","author":"Segler","year":"2018","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0161","doi-asserted-by":"crossref","first-page":"2685","DOI":"10.1016\/j.xphs.2015.12.006","article-title":"Theoretical analysis of drug dissolution: i. solubility and intrinsic dissolution rate","volume":"105","author":"Shekunov","year":"2016","journal-title":"J. Pharm. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0162","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1021\/acs.jmedchem.8b00686","article-title":"Two decades under the influence of the rule of five and the changing properties of approved oral drugs","volume":"62","author":"Shultz","year":"2019","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejps.2026.107688_bib0163","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/17512433.2024.2317963","article-title":"Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists","volume":"17","author":"Singh","year":"2024","journal-title":"Expert Rev. Clin. Pharmacol."},{"key":"10.1016\/j.ejps.2026.107688_bib0164","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1021\/acs.molpharmaceut.5b00861","article-title":"In silico modeling of gastrointestinal drug absorption: predictive performance of three physiologically based absorption models","volume":"13","author":"Sj\u00f6gren","year":"2016","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0165","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-11392-1","article-title":"Machine learning analysis of molecular dynamics properties influencing drug solubility","volume":"15","author":"Sodaei","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejps.2026.107688_bib0166","doi-asserted-by":"crossref","first-page":"4218","DOI":"10.1038\/s41598-024-82728-6","article-title":"Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles","volume":"15","author":"Sun","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejps.2026.107688_bib0167","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae416","article-title":"ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries","volume":"40","author":"Swanson","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejps.2026.107688_bib0168","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1021\/acs.jcim.4c00247","article-title":"Recent advances in automated structure-based de novo drug design","volume":"64","author":"Tang","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0169","doi-asserted-by":"crossref","first-page":"1","DOI":"10.33160\/yam.2026.02.001","article-title":"Next-generation artificial intelligence for ADME prediction in drug discovery: from small molecules to biologics","volume":"69","author":"Tanihata","year":"2026","journal-title":"Yonago Acta Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0170","doi-asserted-by":"crossref","first-page":"125ra31","DOI":"10.1126\/scitranslmed.3003377","article-title":"Data-driven prediction of drug effects and interactions","volume":"4","author":"Tatonetti","year":"2012","journal-title":"Sci. Transl. Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0171","unstructured":"ThermoFisher, Quadrant 2\u2122: Predictive Platform for Solubility and Bioavailability Enhancement. https:\/\/www.patheon.com\/us\/en\/insights-resources\/fact-sheets\/quadrant-2-predictive-platform-for-solubility-and-bioavailability-enhancement.html (accessed 30 June 2026)."},{"key":"10.1016\/j.ejps.2026.107688_bib0172","doi-asserted-by":"crossref","DOI":"10.1208\/s12248-014-9589-4","article-title":"In vitro lipolysis data does not adequately predict the in vivo performance of lipid-based drug delivery systems containing fenofibrate","volume":"16","author":"Thomas","year":"2014","journal-title":"AAPS J."},{"key":"10.1016\/j.ejps.2026.107688_bib0173","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.cherd.2023.07.003","article-title":"Digital process design to define and deliver pharmaceutical particle attributes","volume":"196","author":"Urwin","year":"2023","journal-title":"Chem. Eng. Res. Des."},{"key":"10.1016\/j.ejps.2026.107688_bib0174","series-title":"Revised inventorship guidance for AI-assisted inventions","article-title":"United States Patent and Trademark Office","year":"2025"},{"key":"10.1016\/j.ejps.2026.107688_bib0175","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0176","doi-asserted-by":"crossref","DOI":"10.1080\/19420862.2021.2013593","article-title":"Harnessing the potential of machine learning for advancing \u201cQuality by Design\u201d in biomanufacturing","volume":"14","author":"Walsh","year":"2022","journal-title":"mAbs"},{"key":"10.1016\/j.ejps.2026.107688_bib0177","doi-asserted-by":"crossref","DOI":"10.1111\/cts.70150","article-title":"Predicting pharmacokinetics in rats using machine learning: a comparative study between empirical, compartmental, and pbpk-based approaches","volume":"18","author":"Walter","year":"2025","journal-title":"Clin. Transl. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0178","doi-asserted-by":"crossref","unstructured":"Wang, L., Ejedenawe, O., Lheureux, S., Rodin, D., Allen, C., 2024. Racial diversity in cancer models: a call to action for nanomedicine researchers. https:\/\/doi.org\/10.26434\/chemrxiv-2024-lq0m0.","DOI":"10.26434\/chemrxiv-2024-lq0m0"},{"key":"10.1016\/j.ejps.2026.107688_bib0179","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1039\/D3DD00233K","article-title":"An equivariant graph neural network for the elasticity tensors of all seven crystal systems","volume":"3","author":"Wen","year":"2024","journal-title":"Digit. Discov."},{"key":"10.1016\/j.ejps.2026.107688_bib0180","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.ejpb.2013.01.009","article-title":"Miniaturized screening of polymers for amorphous drug stabilization (SPADS): rapid assessment of solid dispersion systems","volume":"84","author":"Wyttenbach","year":"2013","journal-title":"Eur. J. Pharm. Biopharm. Off. J. Arbeitsgemeinschaft Pharm. Verfahrenstechnik EV"},{"key":"10.1016\/j.ejps.2026.107688_bib0181","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.apsb.2021.03.006","article-title":"Gastrointestinal lipolysis and trans-epithelial transport of SMEDDS via oral route","volume":"11","author":"Xia","year":"2021","journal-title":"Acta Pharm. Sin. B"},{"key":"10.1016\/j.ejps.2026.107688_bib0182","doi-asserted-by":"crossref","first-page":"21660","DOI":"10.1021\/acsami.7b04916","article-title":"Size-dependent translocation of nanoemulsions via oral delivery","volume":"9","author":"Xia","year":"2017","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"10.1016\/j.ejps.2026.107688_bib0184","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejmech.2024.116735","article-title":"Progress in the application of artificial intelligence in molecular generation models based on protein structure","volume":"277","author":"Xu","year":"2024","journal-title":"Eur. J. Med. Chem."},{"issue":"8","key":"10.1016\/j.ejps.2026.107688_bib0183","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1038\/s41591-025-03743-2","article-title":"A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial","volume":"31","author":"Xu","year":"2025","journal-title":"Nat Med."},{"key":"10.1016\/j.ejps.2026.107688_bib0185","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1021\/acs.chemrestox.0c00006","article-title":"Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery","volume":"33","author":"Yang","year":"2020","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejps.2026.107688_bib0186","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejps.2026.107688_bib0187","doi-asserted-by":"crossref","DOI":"10.1016\/j.addr.2023.114697","article-title":"Second near-infrared window fluorescence nanoprobes for deep-tissue in vivo multiplexed bioimaging","volume":"193","author":"Yang","year":"2023","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejps.2026.107688_bib0188","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.ejps.2026.107688_bib0189","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/19466315.2020.1797867","article-title":"Machine learning for clinical trials in the era of COVID-19","volume":"12","author":"Zame","year":"2020","journal-title":"Stat. Biopharm. Res."},{"key":"10.1016\/j.ejps.2026.107688_bib0190","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.ejpb.2019.06.010","article-title":"In vivo models and decision trees for formulation development in early drug development: a review of current practices and recommendations for biopharmaceutical development","volume":"142","author":"Zane","year":"2019","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0191","doi-asserted-by":"crossref","DOI":"10.1002\/smtd.202402249","article-title":"Tracking in vivo lipolysis of lipid nanocarriers using NIR-II polarity-sensitive fluorescent probes","volume":"9","author":"Zhan","year":"2025","journal-title":"Small Methods"},{"key":"10.1016\/j.ejps.2026.107688_bib0192","first-page":"1364","article-title":"Predicting tumor cell response to synergistic drug combinations using a novel simplified deep learning model","author":"Zhang","year":"2020","journal-title":"AMIA Annu. Symp. Proc. AMIA Symp."},{"key":"10.1016\/j.ejps.2026.107688_bib0193","doi-asserted-by":"crossref","DOI":"10.1002\/VIW.20250037","article-title":"Head-to-head comparison between ACQ and FRET in probing polymeric micelles","volume":"6","author":"Zhang","year":"2025","journal-title":"VIEW"},{"key":"10.1016\/j.ejps.2026.107688_bib0194","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1186\/s12951-024-02761-5","article-title":"In vivo fluorescence imaging of nanocarriers in near-infrared window II based on aggregation-caused quenching","volume":"22","author":"Zhang","year":"2024","journal-title":"J. Nanobiotechnology"},{"key":"10.1016\/j.ejps.2026.107688_bib0195","doi-asserted-by":"crossref","first-page":"33288","DOI":"10.1021\/acsnano.5c09066","article-title":"TuNa-AI: a hybrid kernel machine to design tunable nanoparticles for drug delivery","volume":"19","author":"Zhang","year":"2025","journal-title":"ACS Nano"},{"key":"10.1016\/j.ejps.2026.107688_bib0196","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1002\/jat.4141","article-title":"In silico prediction of mitochondrial toxicity of chemicals using machine learning methods","volume":"41","author":"Zhao","year":"2021","journal-title":"J. Appl. Toxicol. JAT"},{"key":"10.1016\/j.ejps.2026.107688_bib0197","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.ejps.2026.107688_bib0198","doi-asserted-by":"crossref","first-page":"35209","DOI":"10.1021\/acsomega.4c06163","article-title":"Revisiting the application of machine learning approaches in predicting aqueous solubility","volume":"9","author":"Zheng","year":"2024","journal-title":"ACS Omega"},{"key":"10.1016\/j.ejps.2026.107688_bib0199","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202511667","article-title":"Rethinking the AI paradigm for solubility prediction of drug\u2011like compounds with dual-perspective modeling and experimental validation","volume":"12","author":"Zhu","year":"2025","journal-title":"Adv. Sci."},{"key":"10.1016\/j.ejps.2026.107688_bib0200","doi-asserted-by":"crossref","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","article-title":"Modeling polypharmacy side effects with graph convolutional networks","volume":"34","author":"Zitnik","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejps.2026.107688_bib0201","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpharm.2025.126432","article-title":"A quantitative size stability metrics for long-acting suspensions and its prediction with machine learning","volume":"687","author":"Zulbeari","year":"2026","journal-title":"Int. J. Pharm."},{"key":"10.1016\/j.ejps.2026.107688_bib0202","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpharm.2024.124967","article-title":"Machine learning strengthened formulation design of pharmaceutical suspensions","volume":"668","author":"Zulbeari","year":"2025","journal-title":"Int. J. Pharm."}],"container-title":["European Journal of Pharmaceutical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0928098726002629?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0928098726002629?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T17:57:01Z","timestamp":1793123821000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0928098726002629"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":201,"alternative-id":["S0928098726002629"],"URL":"https:\/\/doi.org\/10.1016\/j.ejps.2026.107688","relation":{},"ISSN":["0928-0987"],"issn-type":[{"value":"0928-0987","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence and machine learning, a game changer in pharmaceutical development? \u2013 An updated perspective arising from Journees Galeniques 2024","name":"articletitle","label":"Article Title"},{"value":"European Journal of Pharmaceutical Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejps.2026.107688","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Crown Copyright \u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"107688"}}