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

通过人工智能/机器学习预测反应结果采用绿色溶剂

• 批准号: EP/X021033/1
• 负责人: Bao Nguyen
• 金额: $ 202.57万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX021033%2F1
• 关键词: Adopting; Green; Solvents; through; Predicting

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.

从传统的有机溶剂（其中许多都是危险，挥发性或不可持续的）转变为现代绿色溶剂是高价值化学生产中的关键可持续性目标之一。当前，在过程开发阶段通常会探索绿色溶剂的使用，而不是发现阶段。由于产量，选择性，杂质概况和纯化的变化，这需要对过程进行重新估算。这些导致了更长的开发时间，成本和额外的不确定性。另一方面，尽早选择右溶剂可能会增强化学选择性，避免其他反应步骤并简化产品的纯化。预测这些变化是更广泛适应制造中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稀缺性是开发这种能力的关键障碍。因此，迫切需要ML模型根据传统溶剂中的可用数据来预测绿色溶剂的反应性。除了解决早期过程发展的短时间之外，这些还将增加在发现阶段利用绿色溶剂的信心随着可靠的反应时间和税率定律的包含。 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溶剂。该项目将在化学信息和AI/机器学习领域的工业合作伙伴的支持下进行，例如Lhasa Ltd.和Molecule One。它的产量将由高价值化学制造业最终用户的合作伙伴指导和利用：阿斯利康，CATSCI和概念生命科学。