A Hybrid Statistical/Mechanistic Approach to Predicting Reaction Conditions

预测反应条件的混合统计/机械方法

• 批准号: 2276995
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2276995
• 关键词: Hybrid; Statistical; Mechanistic; Approach; Predicting

Year 1: Generic training activities for all first-year student members of the CDT.Year 2-4: The project will work towards building a statistical/mechanistic hybrid model to predict the conditions necessary for C-H activation of pyridines in the ortho-position to the nitrogen-atom, alongside two related challenges: defining the molecular context of a reaction and how to collaborate with others using the Unified Data Model (UDM). Molecular context can be defined using six unique parameter headers: information, energy, time, space, structure and substance. To better understand how to incorporate the structure of relevant compounds in a model, a review of molecular representation will be conducted. Representation of molecules was thought solved, however advancements in Machine Learning (ML) have brought about a resurgence of interest, particularly for the generation of novel compounds. Furthermore, a case study will show how UDM can be used to represent the data necessary for ML driven solvent selection. UDM is an XML-based data format capable of representing most classes of data relevant to chemistry. Building better prediction models for reactivity can aid in retrosynthesis, thus helping chemists in the lab, and is also an enabling factor for building autonomous synthesis platforms. There is precedent for the use of highly accurate reactivity models which are only valid in a small area of chemical space, and developing the concept of 'molecular context' can ease the meshing of such reactivity models.

第1年：针对CDT所有一年级学生成员的通用培训活动。第2至第4年：该项目将致力于建立一个统计/机械混合模型，以预测与氮原子邻位的吡啶的C-H活化所需的条件，以及两个相关的挑战：确定反应的分子背景以及如何使用统一数据模型(UDM)与他人合作。分子上下文可以使用六个唯一的参数报头来定义：信息、能量、时间、空间、结构和物质。为了更好地了解如何将相关化合物的结构纳入模型中，将对分子表示进行审查。分子的表示被认为是解决了问题，然而机器学习(ML)的进步带来了人们的兴趣的复苏，特别是对新化合物的产生。此外，一个案例研究将展示如何使用UDM来表示ML驱动的溶剂选择所需的数据。UDM是一种基于XML的数据格式，能够表示与化学相关的大多数类别的数据。建立更好的反应性预测模型可以帮助反向合成，从而帮助实验室的化学家，也是建立自主合成平台的一个促成因素。使用高精度的反应性模型是有先例的，这种模型只在一小部分化学空间中有效，而发展“分子背景”的概念可以简化这种反应性模型的网格化。