A Data Science Approach to Organic Reaction Discovery and Development

有机反应发现和开发的数据科学方法

• 批准号: RGPIN-2021-02837
• 负责人: Reid, Jolene
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741747
• 关键词: Data; Science; Approach; Organic; Reaction

Reaction development is focused on the identification of optimal conditions that facilitates the conversion of starting materials to a desired product. The application of conditions from closely-related reactions (e.g. those involving similar substrates) to the target transformation largely drives the current process of method development. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Therefore, the optimization process continues to be an empirical endeavor. Our program plans to address this challenge by developing new data science approaches capable of predicting and interpreting reaction outcomes. This work would have three key impacts in chemical synthesis: 1) streamlining the costly process of reaction optimization, 2) enable reaction application to complex substrates. And 3) as the data driven tools utilize physical organic methods to define molecules mathematically, the resulting correlation can be interpreted to provide insights into how catalysts/substrates interact. In our approach, DFT-derived parameter sets describing the structural features of the reaction components are related to experimental outputs. The resulting equation can be deployed to predict experimental outcomes when these components are altered. The reactions and catalysts under study will range significantly in structure and application, but the general goal is to understand the interactions responsible for effective catalysis and to develop new data-driven tools that will facilitate reaction design. This proposal will be driven by the investigation of two critical new ideas. The first is to build data science workflows with enhanced prediction tools for reaction screening to enable rapid identification of optimal conditions for catalysis. The second asks questions pertaining to the generality of selectivity determining interactions in mechanistically disparate reactions. Are the non-covalent interactions governing the enantioselectivity outcomes with similar catalyst or substrate types fundamentally the same? Answering this important question will have long-ranging implications on reaction modeling and allow us to solve the following specific problems: 1) How far "out-of-sample" can the model(s) extrapolate? 2) How can different catalyst chemotypes be included in the same correlation? 3) Can general mechanistic models be developed? In sum, we propose a research program that aims to develop and advance predictive strategies to solve key problems in organic synthesis. Ultimately, such tools will enhance our ability to apply known reactions to complex targets and develop new methods, immediately impacting how one performs experiments in the laboratory. This research will provide unique training opportunities for the next generation of modern organic chemists and will lead to benefits for Canadians by developing new methods for streamlining the reaction optimization that could be implemented in industrial settings.

反应开发的重点是确定促进起始材料转化为所需产品的最佳条件。将密切相关的反应（例如涉及相似底物的反应）条件应用于目标转化在很大程度上推动了当前方法开发的过程。不幸的是，由于反应要求的细微差别，这种方法常常失败。因此，优化过程仍然是一个经验性的努力。我们的计划计划通过开发能够预测和解释反应结果的新数据科学方法来应对这一挑战。这项工作将对化学合成产生三个关键影响：1）简化昂贵的反应优化过程，2）使反应能够应用于复杂的底物。 3）由于数据驱动工具利用物理有机方法以数学方式定义分子，因此可以解释所得的相关性以深入了解催化剂/底物如何相互作用。 在我们的方法中，描述反应组分结构特征的 DFT 派生参数集与实验输出相关。当这些成分发生改变时，所得方程可用于预测实验结果。正在研究的反应和催化剂在结构和应用方面会有很大差异，但总体目标是了解有效催化的相互作用，并开发新的数据驱动工具以促进反应设计。该提案将由对两个关键新想法的调查推动。第一个是使用增强的反应筛选预测工具构建数据科学工作流程，以快速识别最佳催化条件。第二个问题提出了与选择性决定机械不同反应中相互作用的普遍性有关的问题。类似催化剂或底物类型控制对映选择性结果的非共价相互作用是否基本相同？回答这个重要问题将对反应建模产生长期影响，并使我们能够解决以下具体问题：1）模型可以推断“样本外”多远？ 2）不同的催化剂化学类型如何包含在相同的相关性中？ 3）可以开发通用的机械模型吗？ 总之，我们提出了一个研究计划，旨在开发和推进预测策略来解决有机合成中的关键问题。最终，这些工具将增强我们将已知反应应用于复杂目标并开发新方法的能力，从而立即影响人们在实验室中进行实验的方式。这项研究将为下一代现代有机化学家提供独特的培训机会，并将通过开发可在工业环境中实施的简化反应优化的新方法，为加拿大人带来好处。