{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:01:22Z","timestamp":1784797282199,"version":"3.55.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH), lack effective targeted therapies despite increasing global prevalence. The farnesoid X receptor (FXR) is a key regulator of hepatic lipid metabolism, inflammation, and fibrosis, making it a promising therapeutic target. However, existing FXR modulators are associated with limited efficacy and adverse effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to establish a sequential clinical-to-molecular framework combining clinical AI-assisted liver-disease feature learning, FXR target prioritization, WADDAICA-guided molecular design, and experimental validation of a berberine-inspired analogue.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An SE attention-based Transformer model was trained on the Indian Liver Patient Dataset only for liver-disease classification and clinical feature-prioritization analysis. The model output consisted of disease-classification probability and feature-importance information; it was not used for compound optimization. Molecular design of the berberine-inspired analogue was performed independently using the WADDAICA platform.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The AI model demonstrated excellent performance (AUC = 0.9998), identifying clinically relevant metabolic markers linked to FXR pathways. The WADDAICA-designed berberine-inspired analogue showed favorable predicted interactions with FXR, supported by MD and DFT analyses. Favorable pharmacokinetic properties, including high gastrointestinal absorption and low blood\u2013brain barrier permeability, were observed.\n                      <jats:italic>In vitro<\/jats:italic>\n                      , the compound significantly improved cell viability, reduced lipid accumulation, and modulated key molecular markers, including upregulation of FXR and PPAR\u03b1 and downregulation of SREBP-1c, TNF-\u03b1, IL-6, and fibrotic markers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study presents a multi-scale, AI-integrated pipeline linking clinical prediction with molecular design and biological validation. The WADDAICA-designed berberine-inspired analogue showed FXR-associated molecular changes and preliminary cytoprotective and anti-steatotic effects in an FFA-induced HepG2 model. These findings support further investigation of its putative FXR-mediated mechanism of action using dedicated receptor functional assays.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2026.1862990","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T08:08:54Z","timestamp":1784794134000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical AI-assisted FXR prioritization and WADDAICA-guided design of a berberine-inspired analogue for MASH: integrated computational and experimental validation"],"prefix":"10.3389","volume":"17","author":[{"given":"Hui","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Geriatric Medicine, Wuhu City Second People\u2019s Hospital (Affiliated Wuhu Hospital of East China Normal University)","place":["Wuhu, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Neurology, The Affiliated Chuzhou Hospital of Anhui Medical University (The First People\u2019s Hospital of Chuzhou)","place":["Chuzhou, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University","place":["XuZhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man","family":"Ni","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University","place":["XuZhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University","place":["XuZhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Outpatient Clinic, The Affiliated Chuzhou Hospital of Anhui Medical University (The First People\u2019s Hospital of Chuzhou)","place":["Chuzhou, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/s41575-019-0145-7","article-title":"From NASH to HCC: current concepts and future challenges","volume":"16","author":"Anstee","year":"2019","journal-title":"Nat. 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