{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T04:25:45Z","timestamp":1787199945151,"version":"build-2736575974"},"reference-count":36,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp; Syst Pharma"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Apparent permeability () of a drug molecule serves as a principal indicator of drug absorption for pharmacokinetic modeling, and accurate prediction of  values using machine learning would avoid time\u2010consuming and expensive In\u00a0Vitro experiments. In this study, four different feature representations were compared for predicting  values, with each representation serving as input to three subsequent regression models. The best predictive model achieved an  value of 0.68 and RMSE of 0.42 on the test data. Furthermore, the SHAP values corresponding to individual molecular descriptors were analyzed to interpret the influence of these descriptors on the model prediction. Some key properties like octanol\u2013water partition coefficient and number of basic atoms were found to have a strong influence on the  value. Additionally, some specific molecular substructures were identified that contribute either positively or negatively to the model output, thereby inferring effects on drug permeability. Finally, the uncertainty in the predictions was quantified through a distribution\u2010free and model\u2010agnostic method,\n                    <jats:italic>jackknife+<\/jats:italic>\n                    , used for the estimation of confidence intervals. At the optimal experimental setting, the confidence intervals derived using the overall best performing model covered 96.12% and 86.73% of the original  values with more than 95% confidence for test and independent data, respectively. With the same confidence, the confidence intervals contained 99.87% and 100% of the predicted  values for the test and independent data, respectively, underscoring the reliability of the model. Overall, the presented ML provides the most robust informed  prediction among compared models presented to date.\n                  <\/jats:p>","DOI":"10.1002\/psp4.70305","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T17:43:21Z","timestamp":1786643001000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0854-5045","authenticated-orcid":false,"given":"Rajdeep","family":"Mondal","sequence":"first","affiliation":[{"name":"Ramakrishna Mission Vivekananda Educational and Research Institute  Belur India"},{"name":"Institute for Advancing Intelligence, TCG CREST  Kolkata India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6015-5712","authenticated-orcid":false,"given":"Rajith K. R.","family":"Rajoli","sequence":"additional","affiliation":[{"name":"Centre of Excellence for Long\u2010Acting Therapeutics, University of Liverpool  Liverpool UK"},{"name":"Department of Pharmacology and Therapeutics University of Liverpool  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Owen","sequence":"additional","affiliation":[{"name":"Centre of Excellence for Long\u2010Acting Therapeutics, University of Liverpool  Liverpool UK"},{"name":"Department of Pharmacology and Therapeutics University of Liverpool  Liverpool UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumitra","family":"Samanta","sequence":"additional","affiliation":[{"name":"Ramakrishna Mission Vivekananda Educational and Research Institute  Belur India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/mi.2010.5"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/a:1012196216736"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2007.303"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics14101998"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.70137"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.70128"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.13313"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00642"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1039\/D0RA08209K"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00568"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/minf.202400079"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551\u2010018\u20100304\u20100"},{"key":"e_1_2_10_15_1","volume-title":"RDKit: Open\u2010Source Cheminformatics","author":"Landrum G.","year":"2015"},{"key":"e_1_2_10_16_1","unstructured":"T. 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