Recent Progress in Machine Learning Approaches for Predicting Carcinogenicity in Drug Development

doi:10.1080/17425255.2024.2356162

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 期刊論文 > Item 987654321/125647

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题名:	Recent Progress in Machine Learning Approaches for Predicting Carcinogenicity in Drug Development
作者:	Ho, Trang-thi
关键词:	Artificial intelligence;carcinogenicity prediction;drug development;machine learning;predictive modeling;safety assessment;toxicogenomics;computational toxicology
日期:	2024-05-27
上传时间:	2024-07-31 12:06:08 (UTC+8)
出版者:	Taylor and Francis Ltd.
摘要:	Introduction This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. Areas covered The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. Expert opinion Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.
關聯:	Expert Opinion on Drug Metabolism & Toxicology
DOI:	10.1080/17425255.2024.2356162
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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