Patlewicz Grace, Rodford Rosemary, Walker John D
Safety and Environmental Assurance Centre, SEAC, Unilever Colworth, Colworth House, Sharnbrook, Bedford, Bedfordshire MK44 1LQ, United Kingdom.
Environ Toxicol Chem. 2003 Aug;22(8):1885-93. doi: 10.1897/01-461.
Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA).
综述了用于预测致突变性和致癌性的定量构效关系（QSARs）。用于预测致突变性和致癌性的QSARs大多局限于特定类别的化学物质（如芳香胺和杂芳族硝基化学物质）。开发用于预测致突变性和致癌性以筛选化学物质清单的QSARs的动机催生了四个主要的商业化计算机系统，它们能够预测这些终点：基于现有知识的风险演绎估计（DEREK）、计算机辅助技术的毒性预测（TOPKAT）、计算机自动结构评估（CASE）和多重计算机自动结构评估（Multicase）。本文简要概述了这些以及其他一些用于预测致突变性和致癌性的专家系统。其他用于预测致突变性和致癌性的专家系统包括使用模式识别技术的自动数据分析（ADAPT）、QSAR专家系统（QSAR-ES）、化学毒性系统的肿瘤逻辑计算机优化分子参数分析（COMPACT）和共同反应模式（COREPA）。
