基于机器学习的药物毒性预测（科学进展16/2025）

IF 14.3 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Science Pub Date : 2025-04-24 DOI:10.1002/advs.202570112

Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

{"title":"基于机器学习的药物毒性预测（科学进展16/2025）","authors":"Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo","doi":"10.1002/advs.202570112","DOIUrl":null,"url":null,"abstract":"<p><b>Machine Learning</b></p><p>In article number 2413405, Yang Cao, Song He, Xiaochen Bo, and co-workers present the transformative role of artificial intelligence (AI) in drug toxicity prediction. AI models leverage the structural and multiomics features of drugs to predict toxicity while enhancing interpretability, bridging the gap between predictive accuracy and mechanistic understanding. It highlights AI's potential to revolutionize toxicological research and drug development, offering new avenues for safer, more effective therapies.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":117,"journal":{"name":"Advanced Science","volume":"12 16","pages":""},"PeriodicalIF":14.3000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.202570112","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Enabled Drug-Induced Toxicity Prediction (Adv. Sci. 16/2025)\",\"authors\":\"Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo\",\"doi\":\"10.1002/advs.202570112\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Machine Learning</b></p><p>In article number 2413405, Yang Cao, Song He, Xiaochen Bo, and co-workers present the transformative role of artificial intelligence (AI) in drug toxicity prediction. AI models leverage the structural and multiomics features of drugs to predict toxicity while enhancing interpretability, bridging the gap between predictive accuracy and mechanistic understanding. It highlights AI's potential to revolutionize toxicological research and drug development, offering new avenues for safer, more effective therapies.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":117,\"journal\":{\"name\":\"Advanced Science\",\"volume\":\"12 16\",\"pages\":\"\"},\"PeriodicalIF\":14.3000,\"publicationDate\":\"2025-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.202570112\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Science\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/advs.202570112\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Science","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/advs.202570112","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

摘要

在第2413405篇文章中，曹阳、何嵩、薄晓晨及其同事介绍了人工智能（AI）在药物毒性预测中的革命性作用。人工智能模型利用药物的结构和多组学特征来预测毒性，同时增强可解释性，弥合预测准确性和机制理解之间的差距。它强调了人工智能在毒理学研究和药物开发方面的革命性潜力，为更安全、更有效的治疗提供了新的途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine Learning-Enabled Drug-Induced Toxicity Prediction (Adv. Sci. 16/2025)

查看原文本刊更多论文

Machine Learning-Enabled Drug-Induced Toxicity Prediction (Adv. Sci. 16/2025)

Machine Learning

In article number 2413405, Yang Cao, Song He, Xiaochen Bo, and co-workers present the transformative role of artificial intelligence (AI) in drug toxicity prediction. AI models leverage the structural and multiomics features of drugs to predict toxicity while enhancing interpretability, bridging the gap between predictive accuracy and mechanistic understanding. It highlights AI's potential to revolutionize toxicological research and drug development, offering new avenues for safer, more effective therapies.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Advanced Science CHEMISTRY, MULTIDISCIPLINARYNANOSCIENCE &-NANOSCIENCE & NANOTECHNOLOGY

CiteScore

18.90

自引率

2.60%

发文量

1602

审稿时长

1.9 months

期刊介绍： Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.