In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold. Here we present complementary data-driven algorithms to minimize the search in chemical space for phenylthiazole-containing molecules that bind the RNA hairpin within the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis. Our results indicate visual, geometrical, and chemical features that enhance the binding to the targeted RNA. Functional validation was conducted after synthesizing 10 small molecules pinpointed computationally. Four of the 10 were found to be potent inhibitors that target hairpin 91 in the ribosomal PTC of M. tuberculosis and, as a result, stop translation.

中文翻译：

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用于优化 RNA 靶向小分子抑制剂的机器学习方法

在数据科学时代，数据驱动的算法已经成为强大的平台，可以整合生物等排规则，以便对具有通用分子支架的小分子进行优先修饰。在这里，我们提出了互补的数据驱动算法，以尽量减少在化学空间中寻找与结核分枝杆菌核糖体肽基转移酶中心 (PTC) 内的 RNA 发夹结合的苯基噻唑分子。我们的结果表明了增强与目标 RNA 结合的视觉、几何和化学特征。在合成了 10 个通过计算确定的小分子后进行了功能验证。10 种中有 4 种被发现是针对结核分枝杆菌核糖体 PTC 中发夹 91 的有效抑制剂，因此会停止翻译。