Elimination of inadvertent binding is crucial for inhibitor design targeting conserved protein classes like kinases. Compounds in clinical trials provide a rich source for initiating drug design efforts by exploiting such secondary binding events. Considering both aspects, we shifted the selectivity of tozasertib, originally developed against AurA as cancer target, toward the pain target TrkA. First, selectivity-determining features in binding pockets were identified by fusing interaction grids of several key and off-target conformations. A focused library was subsequently created and prioritized using a multiobjective selection scheme that filters for selective and highly active compounds based on orthogonal methods grounded in computational chemistry and machine learning. Eighteen high-ranking compounds were synthesized and experimentally tested. The top-ranked compound has 10000-fold improved selectivity versus AurA, nanomolar cellular activity, and is highly selective in a kinase panel. This was achieved in a single round of automated in silico optimization, highlighting the power of recent advances in computer-aided drug design to automate design and selection processes.

中文翻译：

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通过临床候选者的自动选择性反转，从癌症到疼痛目标

消除意外结合对于靶向保守蛋白类别（如激酶）的抑制剂设计至关重要。通过利用此类次级结合事件，临床试验中的化合物为启动药物设计工作提供了丰富的资源。考虑到这两个方面，我们将最初针对AurA作为癌症靶点开发的tozasertib的选择性朝着疼痛靶点TrkA转移。首先，通过融合几个关键和​​脱靶构象的相互作用网格，确定结合口袋中的选择性决定性特征。随后创建了一个有针对性的库，并使用多目标选择方案确定了优先级，该方案基于基于计算化学和机器学习的正交方法对选择性和高活性化合物进行过滤。合成了18种高级化合物并进行了实验测试。与AurA相比，排名第一的化合物的选择性提高了10000倍，纳摩尔细胞活性高，并且在激酶组中具有高度选择性。这是在单轮自动计算机优化中实现的，突显了计算机辅助药物设计的最新进展对设计和选择过程实现自动化的强大功能。