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RNA时代的计算药物发现

Computational drug discovery under RNA times.

作者信息

Bernetti Mattia, Aguti Riccardo, Bosio Stefano, Recanatini Maurizio, Masetti Matteo, Cavalli Andrea

机构信息

Computational and Chemical Biology, Italian Institute of Technology, 16152 Genova, Italy.

Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, 40126 Bologna, Italy.

出版信息

QRB Discov. 2022 Nov 14;3:e22. doi: 10.1017/qrd.2022.20. eCollection 2022.

Abstract

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.

摘要

RNA分子在细胞中发挥着许多功能和调节作用,因此,作为治疗干预手段,近年来受到了广泛关注。在药物发现领域,基于结构的方法已成功鉴定出与药学相关蛋白质靶点的强效和选择性小分子调节剂。在这里,我们接受使用这些传统方法的计算化学家的观点,并讨论将这些方法扩展到靶向RNA分子所面临的挑战。特别是,我们关注RNA与小分子结合剂之间的识别、选择性以及RNA配体的预期特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eeeb/10392680/10e643ab03d3/S2633289222000205_figAb.jpg

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