基于化学的深度学习模型用于预测非对称化中的立体选择性和绝对配置
Li Cheng1,2, Pan-Lin Shao3, Jiahui Lv1
1Guangdong Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Intelligent Medical Engineering Laboratory, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.
Nature computational science
|December 5, 2025
概括
一个新的深度学习模型ChemAHNet准确地预测了非对称化中的立体选择性和绝对配置. 这种基于化学的方法克服了先前模型对各种催化剂和基质的局限性.
科学领域:
- 有机化学 有机化学
- 计算化学计算化学
- 催化剂是一种催化剂.
背景情况:
- 素的不对称化对于合成性分子至关重要.
- 现有的机器学习模型难以预测复杂反应的立体选择性和绝对配置,特别是那些具有多个前性位点的反应.
- 当前的模型通常依赖于预定义的描述符,并且具有有限的基质-催化剂适用性.
研究的目的:
- 开发一种新的深度学习模型,即化学信息非对称化网络 (ChemAHNet),用于预测烯酸非对称化的结果.
- 克服现有模型在基质-催化剂范围和预测准确性方面的局限性,用于具有多个亲位的反应.
- 为了能够同时预测立体选择性和绝对配置.
主要方法:
- 开发了ChemAHNet,这是一个集反应机制原则的深度学习模型.
- 在深度学习架构中使用了三个结构意识模块.
- 采用简化分子输入线路输入系统 (SMILES) 字符串作为输入,捕获原子级空间和电子相互作用.
- 通过催化剂-olefin 相互作用定义了激活能量屏障 (ΔΔ).
主要成果:
- ChemAHNet准确地预测了主要反体在广泛的催化剂和基质中的绝对配置.
- 该模型成功地定义了不对称化过程的ΔΔ.
- 实现了立体选择性和绝对配置的同时预测.
- 证明了超越烯酸不对称化对其他不对称的催化反应的适用性.
结论:
- ChemAHNet提供了一种强大而准确的方法来预测不对称化结果,超越了以前的机器学习方法.
- 该模型的机制知情设计和处理SMILES输入的能力使其能够捕捉复杂的化学相互作用.
- 在非对称合成中,ChemAHNet提供了针对目标的分子工程和催化剂设计的强大工具.
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