Department of Chemistry, AstraZeneca Pharmaceuticals, 1800 Concord Pike, Wilmington, DE 19850, USA.
J Mol Graph Model. 2010 Nov;29(3):372-81. doi: 10.1016/j.jmgm.2010.07.005. Epub 2010 Jul 30.
We present a technique for computing activity discriminants of in vitro (pharmacological, DMPK, and safety) assays and the application to the prediction of in vitro activities of proposed synthetic targets during the lead optimization phase of drug discovery projects. This technique emulates how medicinal chemists perform SAR analysis and activity prediction. The activity discriminants that are functions of 6 commonly used medicinal chemistry descriptors can be interpreted easily by medicinal chemists. Further, visualization with Spotfire allows medicinal chemists to analyze how the query molecule is related to compounds tested previously, and to evaluate easily the relevance of the activity discriminants to the activities of the query molecule. Validation with all compounds synthesized and tested in AstraZeneca Wilmington since 2006 demonstrates that this approach is useful for prioritizing new synthetic targets for synthesis.
我们提出了一种计算体外（药理学、DMPK 和安全性）测定法活性判别式的技术，并将其应用于在药物发现项目的先导优化阶段预测拟合成靶标的体外活性。该技术模拟了药物化学家如何进行 SAR 分析和活性预测。活性判别式是常用的 6 种药物化学描述符的函数，很容易被药物化学家解释。此外，使用 Spotfire 进行可视化可以使药物化学家分析查询分子与之前测试的化合物之间的关系，并轻松评估活性判别式与查询分子活性的相关性。对阿斯利康威明顿公司 2006 年以来合成和测试的所有化合物进行验证表明，该方法对于为新的合成靶标合成进行优先级排序是有用的。
