为药物发现提供结构信息的机器学习:以任务为中心的视角
Yi Li1,2,3, Rong-Hui Zhan1, Jingxin Rao4
1College of Mathematics and Computer Science, Dali University, No. 2 Hongsheng Road, Dali 671003, China.
Briefings in bioinformatics
|February 23, 2026
概括
深度学习模型正在通过整合蛋白质结构来设计连接体来彻底改变基于结构的药物发现. 这篇评论综合了结构意识的分子建模,重点关注人工智能在创建口袋互补分子方面的进步.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 人工智能在药物发现中的作用
背景情况:
- 蛋白质结构预测的进步使基于结构的药物发现成为可能.
- 深度学习模型包含了针对特定目标的连接体设计的空间约束.
研究的目的:
- 从以任务为中心的角度合成结构意识的分子建模.
- 审查人工智能驱动的药物设计,重点关注结构条件分子生成.
- 为下一代分子建模提供洞察力.
主要方法:
- 专注于绑定口袋识别,交互预测,姿势估计和复杂建模.
- 突出从传统对接到基于几何学的深度学习的演变.
- 将生成方法分类为:基于序列的,基于碎片的,基于图的和基于3D坐标的 (扩散模型).
主要成果:
- 基于几何学的深度学习架构有效地编码蛋白质结构.
- 扩散和点云模型显示出合成口袋互补分子的前景.
- 同折叠模型统一了蛋白质折叠和带结合的预测.
结论:
- 结构性知识正在重塑人工智能驱动的药物设计.
- 挑战包括数据稀缺,概括和多目标控制.
- 未来的方向侧重于可扩展,可解释和物理可信的发电管道.
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