Hasegawa K, Funatsu K
Nippon Roche Research Center, Nippon Roche K.K., Kanagawa, Japan.
SAR QSAR Environ Res. 2000;11(3-4):189-209. doi: 10.1080/10629360008033231.
Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.
综述了基于化学计量学技术的定量构效关系（QSAR）研究。介绍了偏最小二乘法（PLS）作为一种新颖的稳健方法，以取代诸如多元线性回归（MLR）等经典方法。通过典型应用说明了PLS相对于MLR的优势。遗传算法（GA）是一种新颖的优化技术，可作为变量选择中的搜索引擎。描述了我们团队开发的一种包含GA和PLS用于变量选择的新型混合方法（GAPLS）。还介绍了一种更先进的用于比较分子场分析（CoMFA）建模的方法，即基于GA的区域选择（GARGS）。通过一些代表性实例展示了GAPLS和GARGS在QSAR和3D-QSAR问题中的应用。GA可以与非线性建模方法如人工神经网络（ANN）进行杂交，以在化学计量学和QSAR中提供有用的工具。
