Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors,Journal of Chemical Information and Modeling

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Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-11-02 , DOI: 10.1021/acs.jcim.2c00848
Eric R Hantz ₁ , Steffen Lindert ₁

Affiliation

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low μM–nM range, with the most potent being an EGFR inhibitor with an IC₅₀ value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system’s potential energy surface and generate highly predictive receptor conformations.

中文翻译：

基于活性物质的受体选择大大提高了基于结构的药物设计的成功率，并导致鉴定出 22 种有效的癌症抑制剂

计算机辅助药物设计是药物发现管道早期阶段的重要组成部分，它通常会识别大量假阳性结果，这些结果随后被确认为实验性非活性化合物。我们开发了一种方法来提高基于结构的药物设计中的真阳性预测率，并已成功将该协议应用于 20 个目标系统，并为每个目标确定了前三名的性能构象异构体。基于两组独立的已知活性物的接受者操作特征曲线的曲线下面积评估接受者性能。对于五个不同癌症相关疾病目标的子集，我们通过对包含数十万种化合物的小分子库进行盲目筛选的前 50 种化合物进行实验测试，从而验证了我们的方法。我们的受体和化合物选择方法导致在低 μM–nM 范围内鉴定出 22 种新型抑制剂，其中最有效的是具有 IC 的 EGFR 抑制剂₅₀值为 7.96 nM。此外，对于五个独立目标系统的子集，我们展示了高斯加速分子动力学的效用，以彻底探索目标系统的势能面并生成高度预测的受体构象。

更新日期：2022-11-02

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