D3SC: Identification of Products and Pathways in Organic Reactions

在这个由化学系化学结构、动力学机制-B项目资助的项目中，加州大学欧文分校化学系的大卫货车·弗兰肯教授和计算机科学系的皮埃尔·巴尔迪教授正在训练一个名为反应预测器的计算机系统，该系统使用深度学习来识别基于反应物结构的合理逐步反应途径。本研究的目标是开发一种计算工具，快速搜索高度分支的反应途径，以确定有机反应产物的化学结构。识别有机反应的产物将加速药物合成，药物稳定性研究和其他需要合成有机化学的先进制造。该项目位于机械有机化学，化学信息学和机器学习的接口。训练数据基于从高中化学学生到学术研究人员的每个人都能理解的有机反应的常见解释。 该团队有能力为科学领域代表性不足的学生提供教育和培训。大二有机化学专业的学生将参与机器与人类预测应用于有机化学反应的持续进化的基准测试。 预测极性化学反应性的第一阶段是识别亲核电子源原子和亲电子汇原子。化学家目前缺乏一个亲核性和亲电性参数的数据库，该数据库涵盖了亲核官能团的全部范围，从碳-碳键到烷基阴离子，以及亲电官能团的全部范围，从碳-碳σ反键轨道到氰化物阳离子的空轨道。为了填补这一知识空白，甲基离子亲和力的计算方法是将其与溶液相反应性参数相关联，这些相关性值将用于训练源和汇评分中的反应预测器。将评估动态调整的阈值，以适应高反应性或无反应性的源或汇。目前数万个基本反应步骤的训练集将大大扩展。此外，Reaction Predictor将被训练来对任何给定的反应物集合中发生的源-汇对进行排名。Reaction Predictor将使用这些排名来识别实验室中进行的反应的产物和多步反应途径。 此外，反应预测器将在线提供，使强大的用户社区能够参与应用于有机化学的人工智能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In this project, funded by the Chemical Structure, Dynamics & Mechanisms-B Program of the Chemistry Division, Professors David Van Vranken of the Department of Chemistry and Pierre Baldi of the Department of Computer Science at the University of California, Irvine are training a computer system called Reaction Predictor using deep learning to identify plausible stepwise reaction pathways based on the structure of the reactants. The goal of this research is to develop a computational tool that rapidly searches highly branched reaction pathways to identify the chemical structures of the products of organic reactions. Identifying the products of organic reactions would accelerate pharmaceutical synthesis, drug stability studies, and other advanced manufacturing requiring synthetic organic chemistry. The project lies at the interface of mechanistic organic chemistry, chemoinformatics, and machine learning. The training data is based on common depictions of organic reactions understandable by everyone from high school chemistry students to academic researchers. The team is well-positioned to provide education and training for students underrepresented in science. Sophomore organic chemistry students will be engaged in benchmarking the progress in the ongoing evolution of machine vs. human prediction as applied to organic chemistry reactions. The first stage in predicting polar chemical reactivity is the identification of nucleophilic electron source atoms and electrophilic electron sink atoms. Chemists currently lack a database of nucleophilicity and electrophilicity parameters that covers the full span of nucleophilic functional groups, from carbon-carbon bonds to alkyl anions, and the full span of electrophilic functional groups, from carbon-carbon sigma antibonding orbitals to the empty orbitals of a cyanide cation. To fill this void in knowledge, methyl ion affinities are being calculated in a way that correlates them with solution phase reactivity parameters and those correlation values will be used to train Reaction Predictor in source and sink scoring. Dynamically adjusted thresholds will be evaluated to accommodate highly reactive or unreactive source or sinks. The current training set of tens of thousands of elementary reaction steps will be greatly expanded. In addition, Reaction Predictor will be trained to rank source-sink pairs occurring in any given set of reactants. Reaction Predictor will use these rankings to identify products and multistep reaction pathways for reactions carried out in the laboratory. In addition, the Reaction Predictor will be made available on-line, enabling a robust user community to participate in artificial intelligence as applied to organic chemistry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.