从部分到整体:基于碎片的药物发现中人工智能驱动的进展
Jinhyeok Yoo1, Wonkyeong Jang1, Woong-Hee Shin1
1Department of Biomedical Informatics, Korea University College of Medicine, Seoul, 02708, Republic of Korea.
Current opinion in structural biology
|February 19, 2025
概括
基于碎片的药物发现利用人工智能,通过生长,合并和链接分子碎片来有效地识别有力的药物线索. 这种方法增强了大量化学空间的探索,用于新型化合物识别.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 基于碎片的药物发现 (FBDD) 识别了药物开发的强有力的结合碎片.
- 结合碎片可以探索广泛的化学空间,以识别化合物.
- 传统的FBDD方法通过人工智能驱动的分子设计来增强.
研究的目的:
- 审查基于碎片的药物发现的人工智能驱动技术的最新进展.
- 讨论使用人工智能进行碎片组合 (生长,合并,链接) 的方法.
- 突出AI在探索化学空间的潜力,以高效地发现药物.
主要方法:
- 变量自动编码器 (VAE) 是一种自动编码器.
- 强化学习 (RL) 是一种强化学习.
- 在SE(3) -等效模型中.
- 扩散模型的扩散模型.
- 语言模型 语言模型
- 深度进化学习是一种深度进化学习.
- 基于深度学习的链接器优化优化
主要成果:
- 人工智能显著提高了FBDD分子设计的准确性和效率.
- 先进的模型允许精确的分子结构探索和优化.
- 由人工智能驱动的链接器优化增强了打击化合物的开发.
结论:
- 人工智能集成彻底改变了基于碎片的药物发现.
- 这些方法在探索广的化学空间时提供了高效率.
- 人工智能加速了新型化合物的识别.
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