AbbVie, Inc., North Chicago, Illinois 60064, United States.
J Med Chem. 2024 Aug 8;67(15):13174-13186. doi: 10.1021/acs.jmedchem.4c01129. Epub 2024 Jul 25.
Exploring the scope of biocatalytic transformations in the absence of enzyme structures without extensive experimentation is a challenging task. To expand the limited substrate capacity of carrot-mediated bioreduction and hunt for new medicinally relevant ketones with minimum cost of labor and time, we deployed a practical method inspired by ligand-based drug design. Through analyzing collected literature data and building pharmacophore and reactivity prediction models, we screened a self-built virtual library of >8000 ketones bearing the most frequently used -heterocycles and functional groups in drug discovery. Representative examples were validated, expanding the bioreduction substrate scope. The public availability of our models alongside the straightforward screening workflow makes it time-, labor-, and cost-saving to evaluate unknown bioreduction substrates for medicinal chemistry applications, especially for a large set of structurally differentiated ketones. Our studies also showcase the novelty of utilizing medicinal chemistry principles to solve a general biocatalysis problem.
在没有大量实验的情况下探索生物催化转化的范围,而没有酶结构,这是一项具有挑战性的任务。为了扩展胡萝卜介导的生物还原的有限底物容量,并以最小的人力和时间成本寻找新的具有医学相关性的酮类化合物,我们采用了一种受基于配体的药物设计启发的实用方法。通过分析收集的文献数据和构建药效团和反应性预测模型,我们筛选了一个自我构建的包含 >8000 个酮类化合物的虚拟库,这些化合物带有药物发现中最常用的杂环和功能基团。代表性实例得到了验证,扩大了生物还原的底物范围。我们的模型的公开可用性以及简单的筛选工作流程使得评估未知的生物还原底物在药物化学应用中的时间、人力和成本节约成为可能,特别是对于一大组结构不同的酮类化合物。我们的研究还展示了利用药物化学原理解决一般生物催化问题的新颖性。