Harper G, Pickett S D, Green D V S
GlaxoSmithKline, Gunnels Wood Road, Stevenage SG1 2NY, UK.
Comb Chem High Throughput Screen. 2004 Feb;7(1):63-70. doi: 10.2174/138620704772884832.
In this paper we introduce a quantitative model that relates chemical structural similarity to biological activity, and in particular to the activity of lead series of compounds in high-throughput assays. From this model we derive the optimal screening collection make up for a given fixed size of screening collection, and identify the conditions under which a diverse collection of compounds or a collection focusing on particular regions of chemical space are appropriate strategies. We derive from the model a diversity function that may be used to assess compounds for acquisition or libraries for combinatorial synthesis by their ability to complement an existing screening collection. The diversity function is linked directly through the model to the goal of more frequent discovery of lead series from high-throughput screening. We show how the model may also be used to derive relationships between collection size and probabilities of lead discovery in high-throughput screening, and to guide the judicious application of structural filters.
在本文中,我们介绍了一种定量模型,该模型将化学结构相似性与生物活性相关联,特别是与高通量分析中先导化合物系列的活性相关联。从这个模型中,我们推导出了针对给定固定大小的筛选集的最优筛选集组成,并确定了化合物的多样化集合或专注于化学空间特定区域的集合是合适策略的条件。我们从该模型中导出了一个多样性函数,该函数可用于通过其补充现有筛选集的能力来评估用于获取的化合物或用于组合合成的文库。多样性函数通过该模型直接与高通量筛选中更频繁发现先导化合物系列的目标相关联。我们展示了该模型还可如何用于推导筛选集大小与高通量筛选中发现先导化合物的概率之间的关系,并指导结构过滤器的明智应用。