通过相似性内核进行零射击分子生成.
Rokas Elijošius1, Fabian Zills2, Ilyes Batatia3
1Engineering Laboratory, University of Cambridge, Cambridge, UK. re344@cam.ac.uk.
Nature communications
|July 2, 2025
概括
基于分数的生成模型加速化学发现. 这项研究分析了它们的得分功能,揭示了导致一种新方法SiMGen的见解,用于生成具有可控性质的新型分子.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 药物发现 药物发现
背景情况:
- 生成模型,特别是扩散模型,正在推动新的化学发现.
- 它们的成功依赖于得分函数与物理力量的联系,使等价神经网络成为可能.
- 在分子生成中学习得分函数的精确行为仍然未被探索.
研究的目的:
- 分析分数函数在基于能量的扩散模型训练中对分子生成的行为.
- 基于这些见解,开发一种新的零射击分子生成方法.
- 为了实现形状控制和碎片偏差生成,以增强分子设计.
主要方法:
- 训练了一个基于能量的扩散模型来分析分子生成期间的得分函数.
- 开发了基于相似性的分子生成 (SiMGen),一种零射击方法,结合了相似性内核和多体描述符.
- 集成的点云用于形状控制,并利用该方法指导现有的训练模型.
主要成果:
- 在生成过程中,分数函数从恢复潜力过渡到量子力学力.
- 确定了独特的中间分数属性,促进了大分子的构建.
- 在没有重新训练的情况下,SiMGen成功生成了分子,提供了形状控制和碎片偏差.
结论:
- 了解得分函数的动态对于推进生成分子建模至关重要.
- SiMGen为新型分子生成和属性控制提供了一种多功能,无需培训的方法.
- 开发的方法和相关的网络工具 (ZnDraw) 在化学设计中提供了实际应用.
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