Reactivity data for prediction models in organic synthesis

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。尽管最近在机器学习和数据科学方面取得了进展，但由于公开文献中缺乏可靠的动力学和反应性数据，这些模型的开发一直很困难。唯一严谨的反应性数据来源是赫伯特·迈尔的数据库1，但其范围有限。因此，迫切需要：(i)在化学工业和学术界建立健全的全部门反应性数据收集框架，即标准化的反应和测量；（ii）公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。建议项目：我们的目标是通过一个以实验为重点的项目来解决上面强调的问题（结合我的团队正在进行的机器学习活动来进行反应性预测）。该项目的目标是：调整和应用拉萨制定的针对亚硝胺（一类重要的基因毒素）的反应性数据协议。O2：为反应性/机理数据收集开发竞争反应框架。O3：基于非接触光谱学和先进数据处理的高通量动力学/反应性数据采集平台的开发。

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.