生物信息学
计算机科学
化学空间
优势和劣势
监管科学
风险分析(工程)
数据科学
管理科学
专家意见
机器学习
人工智能
药物发现
医学
生物信息学
工程类
生物
心理学
社会心理学
基因
病理
重症监护医学
生物化学
作者
Romualdo Benigni,Arianna Bassan,Manuela Pavan
标识
DOI:10.1080/17425255.2020.1785428
摘要
Introduction Whereas in the past, (Q)SAR methods have been largely used to support the design of new drugs, in the last few decades, there has been a new interest in its applications for the assessment of drug safety. In particular, the ICH M7 guideline has introduced the concept that (Q)SAR predictions for the Ames mutagenicity of drug impurities can be used for regulatory purposes.Areas covered This review introduces the ICH M7 conceptual framework and illustrates the most updated evaluations of the in silico approaches for the prediction of genotoxicity. The strengths and weaknesses of the state-of-the-art are presented and future perspectives are discussed.Expert opinion Given the growing recognition of (Q)SAR approaches, more investment will be devoted to its improvement. The major areas of research should be the expansion and curation of the experimental training sets, with particular attention to the portions of chemical space which are poorly represented. New modeling methodologies (e.g. machine-learning methods) may support this effort, particularly for treating proprietary data without disclosure. Research on new integrative approaches for regulatory decisions will also be important.
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