实施机器学习协议,以使用电子密度拓学的描述符预测有机基质的水解反应特性
Vlada V Petrova1,2, Anton V Domnin2, Yuri B Porozov3,4
1M.M. Shemyakin and Yu.A, Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia.
Journal of computational chemistry
|September 29, 2023
概括
机器学习模型使用分子中的原子量子理论 (QTAIM) 参数准确预测催化抗体效率. 这种方法有助于设计用于捕获有机酸盐农药的新催化剂.
科学领域:
- 计算化学计算化学
- 机器学习在化学中的应用
- 生物催化剂是一种生物催化剂.
背景情况:
- 预测催化反应效率至关重要,但具有挑战性.
- 机器学习 (ML) 为这种预测提供了一个有希望的途径.
- 分子中的原子量子理论 (QTAIM) 为化学系统提供了有价值的描述器.
研究的目的:
- 开发和验证使用QTAIM参数来预测A17 L47K催化抗体的效率的ML策略.
- 评估B97-3c DFT功能对于优化几何形状的适用性.
- 为了将QTAIM描述符与有机酸盐农药的化学类别相关联.
主要方法:
- 使用了ML协议与QTAIM参数相结合.
- 采用B97-3c DFT函数用于几何优化.
- 应用基于密度和层次的集群算法.
- 训练有素的回归和CatBoost算法用于预测.
主要成果:
- B97-3c的功能使得快速准确的几何优化成为可能.
- QTAIM描述符有效地描述了数据集,并与化合物化学类相关联.
- 斜坡回归和CatBoost回归器准确地预测了反应屏障值.
- CatBoost 分类器可靠地区分了活性和非活性化合物.
结论:
- 应用于QTAIM参数的ML是一种可行的策略,用于预测催化抗体效率.
- QTAIM属性为机器学习模型提供物理解释性.
- 这种方法可以加速发现具有所需催化性质的化合物.
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