{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T08:30:12Z","timestamp":1786437012705,"version":"build-2736575974"},"reference-count":70,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"1","funder":[{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["RS-2024-00332003"],"award-info":[{"award-number":["RS-2024-00332003"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["RS-2025-02215961"],"award-info":[{"award-number":["RS-2025-02215961"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2025-16063391"],"award-info":[{"award-number":["RS-2025-16063391"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:p>\n                    Liver microsomal metabolic stability is a key determinant of\n                    <jats:italic toggle=\"yes\">in vivo<\/jats:italic>\n                    exposure and an essential filter in lead optimization, yet cross-species prediction remains difficult because of heterogeneous metabolic pathways and limited model interpretability. We propose a cross-species multitask learning framework that integrates complementary molecular modalities\u2014SMILES-derived fingerprints (Morgan and MACCS\/RDKit), molecular graphs, and\n                    <jats:italic toggle=\"yes\">in silico<\/jats:italic>\n                    absorption, distribution, metabolism, and excretion (ADME)\/physicochemical descriptors\u2014to predict binary microsomal stability (unstable:\n                    <jats:italic toggle=\"yes\">t<\/jats:italic>\n                    <jats:sub>1\/2<\/jats:sub>\n                    \u2264 30 min; stable:\n                    <jats:italic toggle=\"yes\">t<\/jats:italic>\n                    <jats:sub>1\/2<\/jats:sub>\n                    &gt; 30 min) in human (HLM), rat (RLM), and mouse (MLM) liver microsomes. We curated 18,921 PubChem BioAssay measurements (6,685 HLM; 5,753 RLM; 6,483 MLM). Under stratified 10-fold Bemis\u2013Murcko scaffold cross-validation with ensemble prediction and species-specific thresholds, the model achieved AUROC values of 0.811, 0.806, and 0.794 and AUPR values of 0.854, 0.860, and 0.862 for HLM, RLM, and MLM, respectively, consistently outperforming conventional machine-learning and single-task deep-learning baselines. SHapley Additive exPlanations (SHAP) identified transport\/permeability indicators, CYP interaction flags, and the lipophilicity\u2013polarity axis as the features most strongly associated with predicted stability. EdgeSHAPer, a graph neural network explanation method based on SHAP, highlighted stabilizing and destabilizing substructures. Recurrent destabilizing attributions were observed in alkene and allylic\/benzylic contexts, whereas amide\/carbamate motifs exhibited stabilizing attributions, with nitriles and halogens showing context-dependent effects. Fragment\u2013ADME enrichment analysis characterized associations between local structural motifs and whole-molecule properties including lipophilicity, solubility, and blood\u2013brain barrier permeability. This multi-modal, cross-species framework demonstrates that integrating structural encodings with ADME descriptors enhances both predictive performance and interpretability, yielding hypothesis-generating attributions for structural optimization that warrant prospective experimental validation.\n                  <\/jats:p>","DOI":"10.34133\/csbj.0184","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T08:31:08Z","timestamp":1784017868000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Species Multitask Learning with Molecular and ADME Descriptors for Liver Microsomal Metabolic Stability"],"prefix":"10.34133","volume":"35","author":[{"given":"Subhin","family":"Seomun","sequence":"first","affiliation":[{"name":"Department of Intelligent Electronics and Computer Engineering, \rChonnam National University, Gwangju, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0925-1853","authenticated-orcid":true,"given":"Sunyong","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Intelligent Electronics and Computer Engineering, \rChonnam National University, Gwangju, Republic of Korea."},{"name":"R&amp;D Center, \rMATILO AI Inc., Gwangju, Republic of Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"issue":"1","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"20713","DOI":"10.1038\/s41598-020-77327-0","article-title":"Retrospective assessment of rat liver microsomal stability at NCATS: Data and QSAR models","volume":"10","author":"Siramshetty VB","year":"2020","unstructured":"Siramshetty VB, Shah P, Kerns E, Nguyen K, Yu KR, Kabir M, Williams J, Neyra J, Southall N, Nguy\u1ec5n \u00d0-T, et al. 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