Accurate prediction of chemical reactions in solution

准确预测溶液中的化学反应

• 批准号: 2605031
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605031
• 关键词: Accurate; prediction; chemical; reactions; solution

The discovery of new molecules to develop novel materials, agrochemicals, drugs, and therapies is essential to tackle contemporary challenges. However, the rising environmental and economic costs of generating useful novel compounds have placed increased pressure on the chemical sectors. Hence, approaches that can speed up and make the discovery and development processes more efficient are urgently needed. Computational chemistry has become a well-established tool in the development of chemical processes. However, its full potential to transform molecular discovery has been hindered by the limitations of the approaches available. Current methodologies suffer from a lack of generality, accuracy and high cost, making them hard to implement in automated generic workflows. To tackle some of these issues, the Duarte group has developed a computational tool, autodE, which automates the characterisation of reaction pathways using SMILES string representations of reactants and products as inputs. Key features include i) applicability to both organic and organometallic reactions, ii) consideration of conformational sampling of both minima and transition states, and iii) compatibility with several public electronic structure theory packages. However, at present, it is limited by the cost associated with electronic structure methods and solvent description, which hinders its wide use to routinely explore complex catalytic processes and reactions in solution. This is partly due to the description of solvent effects, which is critical in the prediction of chemical reactivity, as solvent effects can bias the preference for a given conformation or reaction pathway. Solvent effects are commonly modelled implicitly with continuum approaches that capture bulk polarisation effects; however, they fail to describe specific solute-solvent interactions, which are crucial to describe charged species. In these cases, explicit solvation may be required to achieve the desired predictive accuracy. The primary objective of this project is to introduce streamlined computational strategies to investigate chemical reactivity in solution, and to use this information to guide reaction optimisation and catalyst design. In collaboration with AstraZeneca, this project seeks to capitalise upon the use of state-of-the-art computational tools and predictive machine learning (ML) models to characterise and predict challenging reactions in solution. More specifically, explicit solvation will be introduced into autodE combining ML potentials for efficient sampling and quadratic string methods to characterise transition states. The implementations developed will be utilised in route design and development and applied for the optimisation of catalysts and substrates in organocatalysed and metal-catalysed reactions. The computational tools arising from this project will be general and widely applicable to different reactions classes and systems, which will accelerate the identification of catalysts and optimal reaction conditions across a wide range of chemical processes. Furthermore, the outcomes of this project will contribute to academic and industrial research in the areas of predictive synthesis, catalysis, process chemistry, computational and theoretical chemistry, and software development. This project falls within the EPSRC Physical Sciences research area.

发现新分子以开发新材料、农用化学品、药物和疗法对于应对当代挑战至关重要。然而，产生有用的新型化合物的环境和经济成本不断上升，给化学部门带来了越来越大的压力。因此，迫切需要能够加快并使发现和开发过程更有效的方法。计算化学已经成为化学过程开发中的一个成熟工具。然而，其改变分子发现的全部潜力受到可用方法的限制的阻碍。目前的方法缺乏通用性、准确性和高成本，使得它们难以在自动化通用工作流程中实现。为了解决其中的一些问题，Duarte小组开发了一种计算工具autodE，它使用反应物和产物的SMILES字符串表示作为输入来自动表征反应途径。主要特点包括i）适用于有机和有机金属反应，ii）考虑最小和过渡态的构象采样，iii）与几个公共电子结构理论软件包的兼容性。然而，目前，它受到与电子结构方法和溶剂描述相关的成本的限制，这阻碍了它在溶液中常规探索复杂催化过程和反应的广泛应用。这部分是由于溶剂效应的描述，这在预测化学反应性中至关重要，因为溶剂效应可以偏向给定构象或反应途径的偏好。溶剂效应通常隐含地建模与连续的方法，捕捉批量极化效应，但是，它们不能描述特定的溶质-溶剂的相互作用，这是至关重要的描述带电物种。在这些情况下，可能需要显式溶剂化来实现期望的预测准确度。该项目的主要目标是引入简化的计算策略来研究溶液中的化学反应性，并使用这些信息来指导反应优化和催化剂设计。该项目与阿斯利康合作，旨在利用最先进的计算工具和预测机器学习（ML）模型来预测和预测溶液中具有挑战性的反应。更具体地说，显式溶剂化将被引入到autodE结合ML潜力的有效采样和二次字符串方法来识别过渡态。开发的实施方案将用于路线设计和开发，并应用于有机催化和金属催化反应中催化剂和底物的优化。从这个项目中产生的计算工具将是通用的，广泛适用于不同的反应类别和系统，这将加速在广泛的化学过程中识别催化剂和最佳反应条件。此外，该项目的成果将有助于预测合成，催化，过程化学，计算和理论化学以及软件开发等领域的学术和工业研究。该项目属于EPSRC物理科学研究领域的福尔斯。