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RNA靶向小分子的发现:挑战与未来方向

Discovery of RNA-Targeting Small Molecules: Challenges and Future Directions.

作者信息

Cai Zhengguo, Ma Hongli, Ye Fengcan, Lei Dingwei, Deng Zhenfeng, Li Yongge, Gu Ruichu, Wen Han

机构信息

DP Technology Beijing China.

School of Mathematics Harbin Institute of Technology Harbin China.

出版信息

MedComm (2020). 2025 Aug 24;6(9):e70342. doi: 10.1002/mco2.70342. eCollection 2025 Sep.

Abstract

RNA-targeting small molecules represent a transformative frontier in drug discovery, offering novel therapeutic avenues for diseases traditionally deemed undruggable. This review explores the latest advancements in the development of RNA-binding small molecules, focusing on the current obstacles and promising avenues for future research. We highlight innovations in RNA structure determination, including X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, which provide the foundation for rational drug design. The role of computational approaches, such as deep learning and molecular docking, is emphasized for enhancing RNA structure prediction and ligand screening efficiency. Additionally, we discuss the utility of focused libraries, DNA-encoded libraries, and small-molecule microarrays in identifying bioactive ligands, alongside the potential of fragment-based drug discovery for exploring chemical space. Emerging strategies, such as RNA degraders and modulators of RNA-protein interactions, are reviewed for their therapeutic promise. Specifically, we underscore the pivotal role of artificial intelligence and machine learning in accelerating discovery and optimizing RNA-targeted therapeutics. By synthesizing these advancements, this review aims to inspire further research and collaboration, unlocking the full potential of RNA-targeting small molecules to revolutionize treatment paradigms for a wide range of diseases.

摘要

靶向RNA的小分子代表了药物发现领域的一个变革性前沿领域,为传统上被认为难以成药的疾病提供了新的治疗途径。本综述探讨了RNA结合小分子开发的最新进展,重点关注当前的障碍和未来研究的有前景的途径。我们强调了RNA结构测定方面的创新,包括X射线晶体学、核磁共振光谱和冷冻电子显微镜,这些为合理的药物设计提供了基础。强调了深度学习和分子对接等计算方法在提高RNA结构预测和配体筛选效率方面的作用。此外,我们讨论了聚焦文库、DNA编码文库和小分子微阵列在识别生物活性配体方面的效用,以及基于片段的药物发现探索化学空间的潜力。对RNA降解剂和RNA-蛋白质相互作用调节剂等新兴策略的治疗前景进行了综述。具体而言,我们强调了人工智能和机器学习在加速发现和优化靶向RNA的治疗药物方面的关键作用。通过综合这些进展,本综述旨在激发进一步的研究与合作,释放靶向RNA的小分子的全部潜力,以彻底改变多种疾病的治疗模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfa0/12375692/f144d961a020/MCO2-6-e70342-g003.jpg

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