推进用于新药标的活性化合物发现:来自人工智能驱动方法的见解
Xing-You Wang1,2, Yang Chen1,2,3, Yu-Fan Li1,2
1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China.
Acta pharmacologica Sinica
|June 17, 2025
概括
人工智能 (AI) 通过改进分子设计,目标识别和用于新疗法的数据分析来加速药物发现. 人工智能提高了寻找具有挑战性的疾病活性化合物的效率和准确性.
科学领域:
- 计算化学和药理学计算化学和药理学
- 药物的发现和开发.
- 医学中的人工智能.
背景情况:
- 发现针对新目标的活性化合物对于开发新疗法至关重要.
- 传统的药物发现方法在效率,准确性和可扩展性方面面临挑战.
- 人工智能 (AI) 提供了克服这些局限性的变革潜力.
研究的目的:
- 提供对人工智能驱动的有效化合物发现方法的全面审查,重点关注新的目标.
- 要突出AI如何解决分子设计,蛋白质结构预测和对接中的瓶.
- 讨论人工智能在连接体探索,表型药物发现和向无药性蛋白质中的作用.
主要方法:
- 对分子设计中人工智能应用的审查,包括蛋白质结构预测和对接.
- 对人工智能驱动的连接物探索技术和数据传输方法的分析.
- 通过多模式数据集成,研究AI在表型药物发现 (PDD) 中的作用.
- 讨论人工智能在开发新型治疗方式,如蛋白质降解剂.
主要成果:
- 人工智能显著提高了活性化合物发现的效率,准确性和可扩展性.
- 人工智能通过准确的结构预测和精确的对接来提高蛋白质感知.
- 人工智能促进了连接体的探索,并通过先进的数据传输弥合了生物和化学空间.
- 人工智能有助于解码复杂的生物系统,并通过综合数据分析驱动PDD.
- 人工智能有助于向以前无法使用的蛋白质,如蛋白质降解剂.
结论:
- 人工智能正在彻底改变针对新目标的活性化合物发现,克服了传统的局限性.
- 人工智能驱动的方法对于在各种疾病中推进下一代治疗方法至关重要.
- 本综述综合了关键的人工智能进展,作为该领域研究人员的资源.
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