Tong Weida, Welsh William J, Shi Leming, Fang Hong, Perkins Roger
Center for Toxicoinformatics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA.
Environ Toxicol Chem. 2003 Aug;22(8):1680-95. doi: 10.1897/01-198.
New techniques and software have enabled ubiquitous use of structure-activity relationships (SARs) in the pharmaceutical industry and toxicological sciences. We review the status of SAR technology by using examples to underscore the advances as well as the unique technical challenges. Applying SAR involves two steps: Characterization of the chemicals under investigation, and application of chemometric approaches to explore data patterns or to establish the relationships between structure and activity. We describe generally but not exhaustively the SAR methodologies popular use in toxicology, including representation of chemical structure, and chemometric techniques where models are both unsupervised and supervised. The utility of SAR technology is most evident when supervised methods are used to predict toxicity of untested chemicals based only on chemical structure. Such models can predict on both an ordinal scale (e.g., active vs inactive) or a continuouis scale (e.g., median lethal dose [LD50] dose). The reader is also referred to a companion paper in this issue that discusses quantitative structure-activity relationship (QSAR) methods that have advanced markedly over the past decade.
新技术和软件已使构效关系(SARs)在制药行业和毒理学领域得到广泛应用。我们通过实例来回顾SAR技术的现状,以突出其进展以及独特的技术挑战。应用SAR涉及两个步骤:对所研究的化学物质进行表征,以及应用化学计量学方法来探索数据模式或建立结构与活性之间的关系。我们将大致但非详尽地描述在毒理学中广泛使用的SAR方法,包括化学结构的表示以及无监督和有监督模型的化学计量学技术。当使用有监督方法仅基于化学结构预测未测试化学物质的毒性时,SAR技术的实用性最为明显。此类模型可以在有序尺度(例如,活性与非活性)或连续尺度(例如,半数致死剂量[LD50])上进行预测。读者还可参考本期的一篇配套论文,该论文讨论了在过去十年中取得显著进展的定量构效关系(QSAR)方法。