Advancing synthesis prediction with machine learning - A data driven/mechanistic approach

通过机器学习推进综合预测 - 数据驱动/机械方法

• 批准号: 2886146
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886146
• 关键词: Advancing; synthesis; prediction; machine; learning

The project will apply the latest machine learning (ML) techniques to chemical applications, including the exploration of reaction pathways toward medicinally relevant scaffolds. The aim will be to develop interpretable ML algorithms that facilitate the prediction of synthetic routes and provide a mechanistic understanding of their outcome.This project will enable the student to explore fundamental scientific questions at the interface of chemistry and machine learning and apply these insights to tackle timely real-world applications. It will also provide the opportunity to work with multi-disciplinary teams in academia and industry. The group of Prof. Fernanda Duarte will provide world-leading expertise in reaction pathway modelling and automation, while the team at IBM Research will bring expertise in the development of computational chemistry software and AI techniques.

该项目将把最新的机器学习（ML）技术应用于化学应用，包括探索与医学相关的支架的反应途径。其目的是开发可解释的ML算法，以促进合成路线的预测，并提供对其结果的机制理解。该项目将使学生能够探索化学和机器学习界面的基础科学问题，并将这些见解及时应用于现实世界的应用。它还将提供与学术界和工业界的多学科团队合作的机会。Fernanda Duarte教授的团队将在反应途径建模和自动化方面提供世界领先的专业知识，而IBM研究院的团队将在计算化学软件和人工智能技术的开发方面带来专业知识。