淡江大學機構典藏:Item 987654321/125647
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125647


    Title: Recent Progress in Machine Learning Approaches for Predicting Carcinogenicity in Drug Development
    Authors: Ho, Trang-thi
    Keywords: Artificial intelligence;carcinogenicity prediction;drug development;machine learning;predictive modeling;safety assessment;toxicogenomics;computational toxicology
    Date: 2024-05-27
    Issue Date: 2024-07-31 12:06:08 (UTC+8)
    Publisher: Taylor and Francis Ltd.
    Abstract: 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.
    Relation: Expert Opinion on Drug Metabolism & Toxicology
    DOI: 10.1080/17425255.2024.2356162
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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