SynFrag:基于药物发现中的碎片组装生成的合成可访问性预测器.
Xiang Zhang1, Jia Liu2, Bufan Xu1,2
1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
Journal of chemical information and modeling
|March 9, 2026
概括
人工智能驱动的药物设计面临着合成差距. 新的模型SynFrag通过学习碎片组装有效地预测分子合成可访问性,克服了现有方法的局限性,以更快地发现药物.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 人工智能驱动的分子生成面临着一个"代合成差距",大多数设计的分子无法在实验中合成.
- 目前的合成可访问性 (SA) 方法,包括CASP工具和ML模型,在计算成本,化学逻辑或性能一致性方面存在局限性.
- 这种差距阻碍了人工智能辅助药物设计 (AIDD) 的应用.
研究的目的:
- 开发一个高效和准确的SA预测模型,以弥合AIDD的生成-合成差距.
- 为了实现快速,大规模的选计算生成的分子合成可行性.
- 为评估药物发现工作流程中的合成难度提供一个可解释的工具.
主要方法:
- 开发了SynFrag,这是一个新的SA预测模型,利用碎片组合自回归生成.
- 在数百万个未标记的分子上进行自我监督的预训,以学习动态碎片组装模式.
- 整合了注意力机制,以识别与合成相关的关键反应部位.
主要成果:
- SynFrag在各种化学领域表现出一致和高性能,包括公共基准,临床药物和人工智能生成的分子.
- 该模型实现了次秒预测时间,使其适用于高通量选.
- 注意力机制通过突出影响合成难度的关键反应点来提供可解释性.
结论:
- 通过提供计算效率高和准确的SA预测,SynFrag有效地解决了生成-合成差距.
- 该模型通过快速过可合成分子来增强AIDD,加速药物发现管道.
- SynFrag为大规模选和详细的合成可访问性评估提供了一个有价值的,可解释的工具.
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