Understanding Solvation Using High Throughput Physical Organic Chemistry

使用高通量物理有机化学了解溶剂化

• 批准号: EP/C545842/1
• 负责人: Christopher Hunter
• 金额: $ 62.27万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FC545842%2F1
• 关键词: Understanding; Solvation; Using; Throughput; Physical

Most chemistry of practical utility and all of biology takes place in solution, yet our fundamental understanding of the role of solvent in these processes remains at a rudimentary level. Qualitative concepts, such as like dissolves like ,d empirical parameters, such as solvent polarity, are widely used to interpret solvents effects on molecularinteractions and reactivity. However, we are currently unable to make quantitative predictions of molecular properties such as solubility or the stability of intermolecular complexes. The applicant recently proposed a new quantitative framework for understanding solvation effects based on pairwise molecular interactions, and this approach shows promise as a predictive tool. The aim of this proposal is to explore the full implications of the model, both experimentally and computationally, to establish methods for making quantitative predictions of solvent effects.The experimental part of the programme will focus on developing a new protocol for measuring interactions between functional groups in a wide range of different solvents. The proposal is to use a chromatographic (hplc) version of the chemical double mutant cycle experiment previously developed by the applicant. This has several advantages over solution-based methods for studying molecular interactions: high solubility is not required, so the range of solvent and functional group combinations that can be studied is greatly expanded; weak interactions that are difficult to detect in solution can be quantified; the system can be coupled to robotic sample handling equipment, so that experiments can be run in an automated high throughput format to generate huge amounts of data rapidly. These experiments will provide the data required to test and refine the basic solvation model discussed above.The computational part of the programme will focus on methods for predicting the experimental behaviour. Two approaches will be investigated: prediction of the intrinsic interaction parameters for functional groups based on calculations on isolated molecules; prediction of interaction energies based on calculations on intermolecular complexes. The data generated in the experimental part of the programme will be used to identify the most promising computational methods, and these will then be refined, guided by the experiments.The ability to make quantitative predictions of solvent effects will have a significant impact in many fields of science and technology, where interactions between molecules are the all important determinants of structure, properties and selectivity.

大多数实用的化学和所有的生物学都是在溶液中进行的，然而我们对溶剂在这些过程中的作用的基本理解仍然处于初级水平。定性概念，如类溶解，经验参数，如溶剂极性，被广泛用于解释溶剂对分子相互作用和反应性的影响。然而，我们目前无法定量预测分子性质，如溶解度或分子间复合物的稳定性。申请人最近提出了一种新的定量框架来理解基于成对分子相互作用的溶剂化效应，这种方法有望成为一种预测工具。本提案的目的是探索该模型的全部含义，包括实验和计算，以建立溶剂效应定量预测的方法。该方案的实验部分将侧重于开发一种新的方案，用于测量各种不同溶剂中官能团之间的相互作用。建议使用申请人先前开发的化学双突变循环实验的色谱（hplc）版本。与基于溶液的方法研究分子相互作用相比，这有几个优点：不需要高溶解度，因此可以研究的溶剂和官能团组合的范围大大扩大；在溶液中难以检测的弱相互作用可以量化；该系统可以与机器人样品处理设备耦合，使实验可以在自动化的高通量格式下运行，快速生成大量数据。这些实验将提供测试和完善上述基本溶剂化模型所需的数据。该计划的计算部分将侧重于预测实验行为的方法。本文将研究两种方法：基于孤立分子的计算来预测官能团的内在相互作用参数；基于分子间配合物计算的相互作用能预测。该计划实验部分产生的数据将用于确定最有前途的计算方法，然后在实验的指导下对这些方法进行改进。对溶剂效应进行定量预测的能力将对许多科学和技术领域产生重大影响，在这些领域中，分子之间的相互作用是结构、性质和选择性的所有重要决定因素。