Adopting Green Solvents through Predicting Reaction Outcomes with AI/Machine Learning

从传统的有机溶剂（其中许多是有害的、易挥发的或不可持续的）转向现代绿色溶剂是高价值化学品制造的关键可持续性目标之一。目前，绿色溶剂的使用往往是在工艺开发阶段进行探索，而不是在发现阶段。由于产率、选择性、杂质分布和纯化的变化，这就需要对工艺进行重新优化。这会导致更长的开发时间、成本和额外的不确定性。另一方面，早期选择合适的溶剂可以提高化学选择性，避免额外的反应步骤，简化产物的纯化。预测这些变化是在制造业中更广泛地适应绿色溶剂的重要基础能力。不幸的是，绿色溶剂中反应数据的缺乏是发展这种能力的主要障碍。因此，迫切需要基于传统溶剂中的可用数据来预测绿色溶剂中的反应性的ML模型。除了解决早期工艺开发的短时间尺度外，这些将增加在发现阶段利用绿色溶剂的信心，支持复杂的合成路线规划工具，该工具考虑了副产品，杂质和纯化方法，并作为评估有害杂质的有价值的监管工具。本项目将通过以下目标解决这些挑战：1通过管理具有可靠反应时间和包含速率定律的反应数据来解决文献中反应性数据的稀缺性问题。建立绿色溶剂依赖溶剂的反应活性和反应选择性预测模型。O3根据工业相关反应的化学信息学分析，制作一套标准底物，并收集其在绿色溶剂中的反应性数据。这些产出将对化学制造行业产生变革性影响，通过更短的开发时间提供快速，更可持续和更好的质量控制过程，以及对绿色溶剂反应结果预测的信心。该项目将在化学信息学和人工智能/机器学习领域的工业合作伙伴的支持下进行，例如拉萨有限公司和Molecule One。其产出将由高价值化学制造部门的最终用户合作伙伴指导和利用：阿斯利康，CatSci和概念生命科学。

The switch from traditional organic solvents, many of which are hazardous, volatile or non-sustainable, to modern green solvents is one of the key sustainability objectives in High Value Chemical Manufacture. Currently, the use of green solvents is often explored at process development stage, instead of discovery stage. This necessitates re-optimisation of processes, due to changes in yield, selectivity, impurity profile and purification. These lead to longer development time, cost, and additional uncertainty. On the other hand, selecting the right solvent early may enhance chemoselectivity, avoid additional reaction steps, and simplify purification of the products.Predicting these changes is an important underpinning capability for wider adaptation of green solvents in manufacturing. Unfortunately, the scarcity of reaction data in green solvents is a key obstacle in developing this capability. Thus, there is an urgent need for ML models which predict reactivity in green solvents based on available data in traditional solvents. In addition to addressing the short time-scale of early-stage process development, these will increase the confidence in utilising green solvents at discovery stage, support sophisticated synthetic routes planning tools which takes into account side products, impurity and purification methods, and act as valuable regulatory tools for assessing hazardous impurities.This project will address these challenges through the following objectives: O1 Addressing the scarcity of reactivity data in the literature through curation of reaction data with reliable reaction time and inclusion of rate laws. O2 Developing solvent-dependent reactivity and reaction selectivity prediction models for green solvents.O3 Producing a set of standard substrates based on cheminformatics analysis of industrially relevant reactions and collecting their reactivity data in green solvents.These outputs will have transformative impacts in the chemical manufacture industry, delivering rapid, more sustainable and better quality-controlled processes through shorter development time, and confidence in predicting reaction outcomes in green solvents. The project will be carried out with support from industrial partners working in the field of cheminformatics and AI/Machine learning, e.g. Lhasa Ltd. and Molecule One. Its outputs will be guided and exploited by partners who are end-users in the High Value Chemical Manufacturing sectors: AstraZeneca, CatSci, and Concept Life Science.