Lewis Richard A
Computer-Aided Drug Design, Eli Lilly and Company Limited, Windlesham, Surrey GU20 6PH, United Kingdom.
J Med Chem. 2005 Mar 10;48(5):1638-48. doi: 10.1021/jm049228d.
Computer-aided drug design tools can generate many useful and powerful models that explain structure-activity relationship (SAR) observations in a quantitative manner. These models can use many different descriptors, functional forms, and methods from simple linear equations through to multilayer neural nets. Using a model, a medicinal chemist can compute an activity, given a structure, but it is much harder to work out what changes are needed to make a structure more active. The impact of a model on the design process would be greatly enhanced if the model were more interpretable to the bench chemist. This paper describes a new protocol for performing automated iterative quantitative structure-activity relationship (QSAR) studies and presents the results of experiments on two QSAR sets from the literature. The fundamental goal of this work is to try to assist the chemist in his search for what to make next.
计算机辅助药物设计工具可以生成许多有用且强大的模型，这些模型以定量方式解释构效关系（SAR）观察结果。这些模型可以使用许多不同的描述符、函数形式和方法，从简单的线性方程到多层神经网络。利用一个模型，药物化学家可以在给定结构的情况下计算活性，但要弄清楚需要进行哪些改变才能使结构更具活性则困难得多。如果模型对实验化学家更具可解释性，那么它对设计过程的影响将大大增强。本文描述了一种用于执行自动迭代定量构效关系（QSAR）研究的新方案，并展示了对文献中两个QSAR数据集进行实验的结果。这项工作的基本目标是尝试协助化学家寻找下一步要合成的物质。
