Structure-based maximal affinity model predicts small-molecule druggability
Structure-based maximal affinity model predicts small-molecule druggability
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DOI：
10.1038/nbt1273
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发表时间：
2007-01-01
影响因子：
46.9
通讯作者：
Huang, Enoch S.
中科院分区：
文献类型：
--
作者：
Cheng, Alan C.;Coleman, Ryan G.;Huang, Enoch S.
铅的产生是小分子药物发现的一个主要障碍，估计有60%的项目因缺乏铅物质或难以优化药物性质的铅而失败。在产生大量支出和努力之前，确定这些不太容易用药的目标是有价值的。在这里，我们表明，一个基于模型的方法，使用基本的生物物理学原理产生良好的预测的药物性能的基础上，仅对目标结合位点的晶体结构。我们定量估计了类药物分子可达到的最大亲和力，并且我们表明这些计算值与药物发现结果相关。我们实验测试两个预测使用高通量筛选的不同化合物的集合。集体成果突出表明了我们的方法以及处理困难目标的战略的效用。
Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.