Reactivity data for prediction models in organic synthesis

有机合成预测模型的反应数据

• 批准号: 2443923
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2443923
• 关键词: Reactivity; data; prediction; models; organic

Predictions models for reactivity have been one of the key underpinning tools for synthetic science. They are critically important for predicting reaction selectivity, synthetic route planning/reagent selection and in predicting impurities in the desired products. The development of these models has been difficult, despite recent advances in machine learning and data science, due to the lack of trustworthy kinetic and reactivity data in the open literature. The only rigorous source of reactivity data is Herbert Mayr's database, 1 but its scope is limited.Thus, there is an urgent needs for: (i) a robust sector-wide reactivity data collection framework, i.e. standardized reactions and measurements, in both chemical industry and academia; (ii) a mechanism to publish and share such data; and (iii) experimental methodologies to generate such data extremely quickly without sacrificing data quality. Proposed Project: we aim to address the issues highlighted above through an experimentally focused project (in conjunction with on-going machine learning activities in my group toward reactivity prediction). The objectives of the project are:O1: Adjustments and application of the reactivity data protocol developed at Lhasa to nitrosamines, an important class of genotoxins.O2: Development of competition reaction framework for reactivity/mechanistic data collection.O3: Development of very high-throughput kinetic/reactivity data collection platforms based on non-contact spectroscopy and advanced data processing.

反应性预测模型一直是合成科学的关键基础工具之一。它们对于预测反应选择性、合成路线规划/试剂选择以及预测所需产品中的杂质至关重要。尽管机器学习和数据科学最近取得了进展，但由于公开文献中缺乏可信的动力学和反应性数据，这些模型的开发一直很困难。唯一严格的反应性数据来源是 Herbert Mayr 的数据库，1 但其范围有限。因此，迫切需要： (i) 化学工业和学术界的强大的全行业反应性数据收集框架，即标准化反应和测量； (ii) 发布和共享此类数据的机制； (iii) 在不牺牲数据质量的情况下极快地生成此类数据的实验方法。拟议项目：我们的目标是通过一个以实验为重点的项目（结合我的小组正在进行的反应性预测机器学习活动）来解决上述强调的问题。该项目的目标是：O1：拉萨开发的亚硝胺反应数据协议的调整和应用，亚硝胺是一类重要的基因毒素。O2：开发反应性/机械数据收集的竞争反应框架。O3：开发基于非接触光谱和先进数据处理的超高通量动力学/反应性数据收集平台。