Department of Chemistry, Duke University, Durham, North Carolina 27708, USA.
Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
RNA. 2023 Apr;29(4):473-488. doi: 10.1261/rna.079497.122. Epub 2023 Jan 24.
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.
RNA 结构调节着生物学和疾病中的广泛过程，但能够调节这些功能的小分子化学探针或药物却很少。机器学习和其他计算方法非常适合填补知识空白，并克服 RNA 靶向的固有挑战，例如 RNA 的动态性质和获得 RNA 高分辨率结构的困难。迄今为止，成功的工具包括主成分分析、线性判别分析、k-最近邻、人工神经网络、多元线性回归等等。这些工具的应用揭示了 RNA：小分子复合物中选择性识别的关键因素、RNA 和蛋白质结合配体的可预测差异，以及允许针对给定 RNA 靶标进行小分子合理设计的定量构效关系。在此，我们提出了利用机器学习和其他计算方法来推进 RNA：小分子靶向的观点，包括选择的实例及其验证，以及必要的和有前途的未来方向，这些方向对于加速这一重要领域的发现将是关键。
