人工智能用于RNA - 连接物相互作用预测:进展和前景
Jing Li1, Yi Tan1, Ruiqiang Lu1
1Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, 999078 Macao, China.
Drug discovery today
|April 26, 2025
概括
人工智能 (AI) 正在通过提高药物发现的预测准确性来改变RNA - 连接体相互作用研究. 本综述涵盖了AI在绑定站点识别,建模和选中的应用,解决当前的挑战和未来的方向.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- RNA-配体相互作用对于生物过程和治疗开发至关重要.
- 预测这些相互作用是复杂的,需要先进的计算方法.
- 人工智能 (AI) 提供了强大的工具来分析RNA-接体动态和潜力.
研究的目的:
- 审查人工智能驱动的方法对RNA - 连接体相互作用预测的进展.
- 突出AI应用在绑定站点识别,结构建模,绑定模式和亲和度预测方面.
- 讨论该领域的挑战和未来的研究方向.
主要方法:
- 关于人工智能应用在RNA-配体相互作用研究中的当前文献的综述.
- 分析用于绑定站点识别,结构建模和虚拟选的AI技术.
- 讨论包括数据稀缺性和RNA灵活性建模在内的挑战.
主要成果:
- 人工智能显著提高了RNA-连接体结合部位识别和结构预测的准确性.
- 人工智能驱动的虚拟查加速了潜在的RNA向药物的发现.
- 关键的挑战仍然在于数据的可用性和准确的RNA动态建模.
结论:
- 人工智能正在彻底改变RNA - 配体相互作用预测,这对生物学理解和药物发现至关重要.
- 未来的工作应该将人工智能与基于物理的模型相结合,并扩展实验数据集.
- 增强的预测能力有望加速新型RNA向疗法的开发.
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