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

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。准确和快速的计算方法来预测化学反应性将提供具有成本效益的替代费时的实验方法，在某些情况下，动物试验，在药物设计，毒理学和化学合成。基于机制的预测模型非常重要，因为与计算“黑箱”模型相比，它们更有可能获得普遍接受，后者无法深入了解如何以及为什么进行预测。提供这样的见解对于模型在毒理学和药物设计中获得监管认可尤为重要。然而，目前的计算方法（分子建模或机器学习（ML））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;这些理想的特性中的一个或多个必须牺牲在追求其他。该项目将开发一种新型的协同分子建模和ML方法，用于快速，高精度和基于机制的反应性预测，用于毒理学，药物设计和化学合成，从而帮助实现“圣杯”。（~ 10，000种化合物），可以将从快速分子建模技术获得的能量障碍校正为从非常缓慢的高精度方法获得的能量障碍。因此，我们对反应建模的协同方法将是从这些快速分子建模技术中获得机理见解，并使用我们的ML模型来获得快速准确的反应屏障。将开发C-N键形成反应模型，用于共价药物设计（靶向赖氨酸）、毒理学（预测致突变性和呼吸致敏性）和药物合成规划。为了证明我们的协同方法的广泛实用性，我们将使用它来合理化生物和合成相关系统的实验反应性数据，使用当前的建模方法将是非常缓慢的。不需要超级计算机，甚至在笔记本电脑上也可以进行预测，这将代表反应建模的范式转变。

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.