Dudek Arkadiusz Z, Arodz Tomasz, Gálvez Jorge
University of Minnesota Medical School, Minneapolis, 55455, USA.
Comb Chem High Throughput Screen. 2006 Mar;9(3):213-28. doi: 10.2174/138620706776055539.
Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.
作为高通量筛选和组合化学的补充方法，组合文库的虚拟筛选和过滤近来受到关注。这些化学信息学技术严重依赖定量构效关系（QSAR）分析，这是一个拥有成熟方法和成功历史的领域。在本综述中，我们讨论构建QSAR模型的计算方法。我们首先概述它们在高通量筛选中的有用性，并确定QSAR模型的总体方案。接下来，我们专注于构建QSAR模型三个主要组成部分的方法，即描述化合物分子结构的方法、选择信息性描述符的方法和活性预测的方法。我们既介绍了成熟的方法，也介绍了最近引入QSAR领域的技术。
