Dixon S L, Villar H O
Telik, Inc., South San Francisco, California 94080, USA.
J Chem Inf Comput Sci. 1998 Nov-Dec;38(6):1192-203. doi: 10.1021/ci980105+.
The Similarity Principle provides the conceptual framework behind most modern approaches to library sampling and design. However, it is often the case that compounds which appear to be very similar structurally may in fact exhibit quite different activities toward a given target. Conversely, some targets recognize a wide variety of molecules and thus bind compounds that have markedly different structures. Affinity fingerprints largely overcome the difficulties associated with selecting compounds on the basis of structure alone. By describing each compound in terms of its binding affinity to a set of functionally dissimilar proteins, fundamental factors relevant to binding and biological activity are automatically encoded. We demonstrate how affinity fingerprints may be used in conjunction with simple algorithms to select active-enriched diverse training sets and to efficiently extract the most active compounds from a large library.
相似性原则为大多数现代库筛选和设计方法提供了概念框架。然而，通常情况下，结构上看似非常相似的化合物实际上对给定靶点可能表现出截然不同的活性。相反，一些靶点能识别各种各样的分子，因此能结合结构明显不同的化合物。亲和力指纹图谱在很大程度上克服了仅基于结构选择化合物所带来的困难。通过根据化合物与一组功能不同的蛋白质的结合亲和力来描述每个化合物，与结合和生物活性相关的基本因素会自动被编码。我们展示了亲和力指纹图谱如何与简单算法结合使用，以选择富含活性的多样化训练集，并从大型库中高效提取最具活性的化合物。
