Investigation of Transition-Metal Catalysed C-H Activation Using Quantum Chemistry and Machine Learning

利用量子化学和机器学习研究过渡金属催化的 C-H 活化

• 批准号: 2759938
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2759938
• 关键词: Investigation; Transition; Metal; Catalysed; Activation

Direct functionalisation of C-H bonds to form new C-C bonds (or C-N, C-O etc.) has received significant interest in the past two decades, particularly in drug discovery. This broad class of reactions is attractive since it enables shorter and more efficient syntheses, avoiding the use of halide/amine pre-functionalised molecules prior to performing their coupling. C-H activation reactions usually require the use of a transition metal (TM) catalyst (e.g., Pd, Pt). The reaction is suggested to proceed via metal insertion at the C-H bond, although alternative mechanisms are possible. However, even with TM catalysts, C-H activation requires harsh reaction conditions due to its high bond dissociation energy, which reduces tolerance of sensitive functional groups. Additionally, there is often poor regio- and stereo-selectivity. The aim of this project is twofold, the first is the automation of reaction path generation for these reactions with quantum chemical methods. The second goal is the prediction of reaction products for given reactants via machine learning (ML) approaches. Computational studies can help elucidate the mechanism of these reactions and thereby allow improving their reactivity and selectivity. Most of the previous work has been done via manual exploration of potential energy surfaces to find transition states (TS), which is time consuming and requires expert users. Automating such analyses would reduce the resources spent on modelling and increase their impact on synthetic development. Efforts to automate the study of this type of reactions are only recently being reported with model systems. This project aims to leverage the utility for C-H activation reactions. We will explore the use semi-empirical (SE) methods to obtain TS guesses and then augment that with ML-based correction to obtain a geometry close to the DFT-optimised TS structure. This combined SE-ML approach has been applied to the prediction of solvation energy, heats of formation etc., and shown to converge faster and require less training data than pure ML models. We will also aim to extend the generalisability of this approach to predict C-H activation with any metal catalyst (i.e., Ru, Fe etc.). The second avenue to explore will be the use of ML methods to predict the reaction products, especially the regio-/stereo-selectivity of C-H activation reaction, given the reactants. Here we will employ the IBM Rxn platform as a starting point, which has been shown to achieve 90% accuracy on the first prediction against the known reaction outcome. This ML approach, introduced by Schwaller et al, consists of a neural network language-transformer model which "translates" SMILES strings of reactants into the product SMILES. Even though ML models to predict general reactions are available, their efficacy for C-H activation reactions has not been extensively tested. We intend to investigate whether language-transformer based ML models can predict C-H activation regioselectivity and stereoselectivity. We will also implement 3D structure-based ML models (e.g., based on molecular electrostatic potential) and test whether it is more successful in predicting selectivity. This project falls within the EPSRC research area "computational and theoretical chemistry". But it also touches upon areas of reaction mechanism, medicinal chemistry, transition metal catalysis, DFT and machine learning. This project aligns with the goals of EPSRC in that area, including collaboration with pharmaceutical sector and software development. The latter will be beneficial to improving computational workflows and the study of catalytic mechanisms which are relevant in the broader scientific context. We are very happy to have AstraZeneca as our industrial collaborators. They assist by suggesting which routes of inquiry might align better with the synthetic and drug discovery goals of pharmaceutical industries.

C-H键的直接功能化以形成新的C-C键（或C-N、C-O等）在过去二十年中引起了极大的兴趣，特别是在药物发现方面。这类反应很有吸引力，因为它可以实现更短、更有效的合成，避免在进行偶联之前使用卤化物/胺预官能化分子。 C-H 活化反应通常需要使用过渡金属 (TM) 催化剂（例如 Pd、Pt）。尽管替代机制是可能的，但建议该反应通过 C-H 键处的金属插入进行。然而，即使使用TM催化剂，由于其高键解离能，C-H活化也需要苛刻的反应条件，这降低了敏感官能团的耐受性。此外，区域选择性和立体选择性通常较差。该项目的目标是双重的，第一个是利用量子化学方法自动生成这些反应的反应路径。第二个目标是通过机器学习 (ML) 方法预测给定反应物的反应产物。计算研究可以帮助阐明这些反应的机制，从而提高它们的反应性和选择性。之前的大部分工作都是通过手动探索势能面来寻找过渡态（TS）来完成的，这非常耗时并且需要专家用户。自动化此类分析将减少建模所花费的资源，并增加其对综合开发的影响。最近才通过模型系统报道了对此类反应进行自动化研究的努力。该项目旨在利用 C-H 活化反应的实用性。我们将探索使用半经验 (SE) 方法来获得 TS 猜测，然后通过基于 ML 的校正对其进行增强，以获得接近 DFT 优化的 TS 结构的几何形状。这种组合的 SE-ML 方法已应用于溶剂化能、生成热等的预测，并且与纯 ML 模型相比，收敛速度更快，需要的训练数据更少。我们还将致力于扩展这种方法的普适性，以预测任何金属催化剂（即 Ru、Fe 等）的 C-H 活化。第二个探索的途径是使用机器学习方法来预测反应产物，特别是给定反应物的 C-H 活化反应的区域/立体选择性。在这里，我们将采用 IBM Rxn 平台作为起点，该平台已被证明可以在针对已知反应结果的首次预测中实现 90% 的准确率。 Schwaller 等人提出的这种机器学习方法由神经网络语言转换器模型组成，该模型将 SMILES 反应物字符串“翻译”为产品 SMILES。尽管可以使用 ML 模型来预测一般反应，但它们对 C-H 激活反应的功效尚未得到广泛测试。我们打算研究基于语言转换器的 ML 模型是否可以预测 C-H 激活区域选择性和立体选择性。我们还将实现基于 3D 结构的 ML 模型（例如，基于分子静电势）并测试它在预测选择性方面是否更成功。该项目属于 EPSRC 研究领域“计算和理论化学”。但它也涉及反应机理、药物化学、过渡金属催化、DFT 和机器学习等领域。该项目符合 EPSRC 在该领域的目标，包括与制药行业和软件开发的合作。后者将有利于改进计算工作流程和与更广泛的科学背景相关的催化机制的研究。我们很高兴阿斯利康成为我们的工业合作伙伴。他们通过建议哪些调查途径可能更好地符合制药行业的合成和药物发现目标来提供帮助。