Machine Learning and Molecular Modelling: A Synergistic Approach to Rapid Reactivity Prediction

机器学习和分子建模：快速反应预测的协同方法

基本信息

批准号：
EP/W003724/1
负责人：
Matthew Grayson
金额：
$ 38.82万
依托单位：
University of Bath
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FW003724%2F1
关键词：
Machine Learning Molecular Modelling Synergistic

项目摘要

The computational design of new chemical reactions is regarded as one of the "Holy Grails" of computational organic chemistry and biochemistry. Accurate and fast computational approaches to predicting chemical reactivity would provide cost-effective alternatives to time-consuming experimental approaches, and in some cases animal testing, in drug design, toxicology and chemical synthesis. Of great importance are mechanism-based prediction models because they are much more likely to reach general acceptance compared to computational "black-box" models which offer no insight into how and why predictions are made. Providing such insight is especially important for models to gain regulatory acceptance in toxicology and drug design. However, no current computational approach (molecular modelling or machine learning (ML)) to reactivity prediction offers the combination of fast, accurate predictions with clear mechanistic insight; one or more of these desirable characteristics must be sacrificed in pursuit of the others. This project will develop a novel, synergistic molecular modelling and ML approach to rapid, high-accuracy and mechanism-based reactivity prediction for use in toxicology, drug design and chemical synthesis and thus help realise the "Holy Grail".We will train and validate ML models on large datasets (~10,000 compounds) that can correct energy barriers obtained from rapid molecular modelling techniques to those derived from prohibitively slow, high-accuracy methods. Our synergistic approach to reaction modelling will thus be to derive mechanistic insight from these rapid molecular modelling techniques and use our ML models to obtain fast and accurate reaction barriers. Models for C-N bond-forming reactions will be developed for use in covalent drug design (targeting lysine), toxicology (predicting mutagenicity and respiratory sensitisation) and pharmaceutical drug synthesis planning. To demonstrate the broad utility of our synergistic approach, we will use it to rationalise experimental reactivity data of biologically and synthetically relevant systems for which the use of current modelling approaches would be prohibitively slow. Rather than requiring a supercomputer, predictions will be possible even on a laptop which will represent a paradigm shift in reaction modelling.

新化学反应的计算设计被认为是计算有机化学和生物化学的“圣杯”之一。预测化学反应性的准确而快速的计算方法将为耗时的实验方法提供具有成本效益的替代方法，在某些情况下，动物测试，药物设计，毒理学和化学合成。非常重要的是基于机制的预测模型，因为与计算“黑盒”模型相比，它们更有可能达到一般性接受，这些模型没有洞悉如何和为什么进行预测。提供这种洞察力对于模型获得毒理学和药物设计的监管接受尤其重要。但是，没有当前的计算方法（分子建模或机器学习（ML））对反应性预测提供了快速，准确的预测与明确的机械洞察力的组合。这些理想的特征中的一个或多个必须在追求其他特征时牺牲。该项目将开发一种新型的，协同的分子建模和ML方法，用于快速，高准确性和基于机制的反应性预测，用于在毒理学，药物设计和化学合成中使用，从而有助于实现“圣杯”。我们将在大型数据集中训练和验证ML模型（〜10,000个化合物），从而可以从这些速度障碍（〜10,000个化合物）上获得，从而可以从这些速度障碍（〜10,000个化合物）中获得技术模型，从而可以通过这些速度来验证这些速度，从而可以从这些速度壁垒（〜10,000个化合物）中获得技术模型。高准确方法。因此，我们的反应建模方法将是从这些快速分子建模技术中得出机械洞察力，并使用我们的ML模型获得快速准确的反应屏障。 C-N形成反应的模型将用于共价药物设计（靶向赖氨酸），毒理学（预测诱变和呼吸敏化）和药物合成计划。为了证明我们的协同方法的广泛效用，我们将使用它来合理化生物学和合成相关系统的实验反应性数据，用于当前建模方法的使用将非常慢。即使在笔记本电脑上也代表反应建模的范式变化，也可以进行预测，而不是要求超级计算机。