Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA.
Ladder Therapeutics, USA.
Angew Chem Int Ed Engl. 2023 Mar 6;62(11):e202211358. doi: 10.1002/anie.202211358. Epub 2023 Feb 6.
Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.
小分子靶向 RNA 已成为药物化学的一个新前沿，但与靶向蛋白质的文献相比，我们对与 RNA 结合的化学物质的理解还很有限。在这项研究中，我们报告了 Repository Of BInders to Nucleic acids (ROBIN)，这是一种通过小分子微阵列 (SMM) 筛选鉴定的新型核酸结合物文库。报告了针对 24572 种小分子文库的 36 个单独核酸 SMM 筛选的完整结果（总共测定了 1627072 个相互作用）。确定了一组 2003 个 RNA 结合小分子，这代表了迄今为止最大的完全公开的、基于实验的同类文库。机器学习被用于开发高度可预测和可解释的模型来描述 RNA 结合分子。这项工作表明，应用于基于实验的 RNA 结合物集合的机器学习算法是一种有前途的方法，可以了解针对 RNA 的化学空间。
