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SynTwins:一个以回复合成为导向的框架,用于合成的分子模拟生成

Shuan Chen^1,2, Gunwook Nam¹, Alán Aspuru-Guzik³

¹Department of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University 1 Gwanak-ro Seoul South Korea.

Chemical science

|December 8, 2025

概括

SynTwins是一个新的AI框架,它设计的分子既有效又易于合成. 这种计算型药物发现工具弥合了人工智能产生的候选药物和现实世界化学合成之间的差距.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
材料科学是一种材料科学.

背景情况:

生成型人工智能加速分子提议,但往往产生无法合成的化合物.
人工智能生成分子的合成可行性是计算发现的一个主要障碍.
弥合计算设计和实验合成之间的差距至关重要.

研究的目的:

介绍SynTwins,一个新的反合成引导分子设计框架.
解决人工智能驱动的分子发现中合成可行性的瓶.
开发一种方法来产生合成可访问的分子类似物.

主要方法:

在三个步骤中模拟专家化学家的策略:反合成,构建块搜索和虚拟合成.
使用搜索算法,而不是随机数据驱动生成器.
将SynTwins集成到现有的分子属性优化框架中.

主要成果:

在探索合成可访问的模拟方面,SynTwins的表现优于最先进的机器学习模型.
保持与原始标分子的高结构相似性.
混合方法可以产生可行的类似物,而物质得分损失最小.

结论:

SynTwins有效地弥合了计算设计和实验合成之间的差距.
为加速合成分子的发现提供了一个实用的解决方案.
适用于药物和材料发现中的广泛应用.

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