Pfizer, Inc., Compound Safety Prediction, Eastern Point Road, Groton, CT 06340, USA.
Expert Opin Drug Metab Toxicol. 2010 Jul;6(7):797-807. doi: 10.1517/17425255.2010.495118.
The computational prediction of genotoxicity is important to the early identification of those chemical entities that have the potential to cause carcinogenicity in humans.
The review discusses key scientific developments in the prediction of Ames mutagenicity and in vitro chromosome damage over the past 4 - 5 years. The performance and limitations of computational approaches are discussed in relation to published and internal validation exercises. Their application to the modern drug discovery paradigm is also discussed.
Key highlights of a review of the recent scientific literature for the prediction of Ames mutagenicity and chromosome damage and an appreciation of the factors that limit the predictive performance of in silico systems.
Current in silico systems perform well in the mutagenicity prediction of the publicly-derived data on which they are based, but their performance outside the applicability domain is considerably lower. We conclude that it is the lack of mechanistic structure-activity relationships and limited access to high quality proprietary data which are holding back computational genotoxicity from reaching higher predictive levels.
计算预测的遗传毒性是很重要的，早期识别的那些化学实体，有可能导致致癌性在人类。
本综述讨论了关键的科学发展预测的 Ames 致突变性和体外染色体损伤在过去的 4 - 5 年。性能和局限性的计算方法进行了讨论，涉及到已发表的和内部验证练习。他们的应用程序，以现代药物发现范例也进行了讨论。
主要亮点的审查最近的科学文献预测的 Ames 致突变性和染色体损伤和欣赏的因素，限制了预测性能的计算机系统。
目前的计算机系统在预测的致突变性方面表现良好公开的基础上的数据，但是他们的表现之外的适用性域是相当低。我们得出的结论是，缺乏机械结构-活性关系和有限的访问高质量的专有数据，阻碍了计算遗传毒性达到更高的预测水平。
