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使用形状条件扩散模型与指导生成3D结合分子
Ziqi Chen1, Bo Peng1, Tianhua Zhai2
1Computer Science and Engineering, The Ohio State University, Columbus, OH 43210.
Nature machine intelligence
|November 19, 2025
概括
一种新的生成人工智能 (genAI) 方法DiffSMol通过设计新的3D分子来加速药物开发. 这种人工智能工具在生成具有所需形状和结合亲和力的分子方面表现出卓越的性能.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 药物开发是一个漫长而昂贵的过程.
- 生成型人工智能 (genAI) 为简化药物发现提供了潜在的解决方案.
- 需要新的方法来产生具有最佳药物候选物的特定性质的分子.
研究的目的:
- 开发一种新的 genAI 方法 DiffSMol,以促进药物开发.
- 根据已知的连接体形状和蛋白质口袋信息生成3D结合分子.
- 提高识别有前途的候选药物的效率和成功率.
主要方法:
- 开发了DiffSMol,一种使用扩散模型和预训练的形状嵌入的genAI方法.
- 采用代形状指导来完善生成的3D分子结构.
- 集成的蛋白质口袋信息,用于指导分子生成,以达到最佳的结合亲和度.
- 对基准数据集的评估性能与最先进的方法对比.
主要成果:
- 使用形状指导的DiffSMol在生成类似于连接体形状的分子方面取得了61.4%的成功率,显著优于基线 (11.2%).
- 与最佳基线相比,DiffSMol与口袋指导的结合亲和度提高了13.2%.
- 将形状和口袋指导结合在一起,进一步提高了约束亲和度17.7%.
- 在案例研究中,产生的分子表现出有利的物理化学和药物动力学特性.
结论:
- DiffSMol在生成具有所需形状和亲和性的3D结合分子方面表现出卓越的性能.
- 该方法显示了加速药物发现和开发有前途的候选药物的巨大潜力.
- DiffSMol为计算化学和人工智能驱动的药物开发提供了一个强大的工具.
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