{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:26:51Z","timestamp":1786955211706,"version":"build-2736575974"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>Background\/Objectives: Transthyretin amyloidosis (ATTR) is a progressive disease caused by the dissociation of the transthyretin (TTR) tetramer, leading to amyloid fibril formation. Although pharmacological stabilizers have been developed, preventive strategies for wild-type ATTR (ATTRwt) have not been established. This study developed a computational model to predict TTR-binding activity from chemical structures and applied it to the exploratory prioritization of food-derived compounds. Methods: A machine learning model was constructed using TTR\u20138-anilino-1-naphthalenesulfonic acid displacement assay data from the Tox24 Challenge. An integrated dataset compiled from multiple literature sources was used to compare predicted TTR-binding activity with amyloid fibril formation inhibition, and the trained model was applied to the PhytoHub database. Results: The model achieved a root mean square error of 21.34 and a mean absolute error of 15.9 percentage points on an external test dataset, a performance comparable to that of the top-scoring models in the Tox24 Challenge. The predicted TTR-binding activity showed positive correlations with inhibition of amyloid fibril formation, Pearson\u2019s r = 0.673 and Spearman\u2019s \u03c1 = 0.602. The model prioritized 39 compounds with high predicted binding activity, predominantly polyphenols. Conclusions: These results suggest that the proposed in silico method may be useful for the exploratory prioritization of food-derived compounds with potential relevance to TTR stabilization.<\/jats:p>","DOI":"10.3390\/ph19081295","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T08:05:41Z","timestamp":1786953941000},"page":"1295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Prediction of Transthyretin-Binding Activity and Its Application to Screening Food-Derived Compounds for ATTR Amyloidosis"],"prefix":"10.3390","volume":"19","author":[{"given":"Yuma","family":"Iwashita","sequence":"first","affiliation":[{"name":"Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5773-991X","authenticated-orcid":false,"given":"Yoshihiro","family":"Uesawa","sequence":"additional","affiliation":[{"name":"Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1080\/13506129.2022.2052838","article-title":"Guidelines and New Directions in the Therapy and Monitoring of ATTRv Amyloidosis","volume":"29","author":"Ando","year":"2022","journal-title":"Amyloid"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.1172\/JCI112224","article-title":"Biochemical Marker in Familial Amyloidotic Polyneuropathy, Portuguese Type. 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