Towards a Universal Molecular Moiety Feature Set from the Topology of the Electron Density

从电子密度拓扑走向通用分子部分特征集

• 批准号: RGPIN-2022-05060
• 负责人: Mawhinney, Robert
• 金额: $ 1.75万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748002
• 关键词: Towards; Universal; Molecular; Moiety; Feature

The goal of chemistry is to develop new molecules and materials that improve our world. While we have seen significant improvements in our world, there have been some very troublesome issues that have resulted in a public that distrusts the chemical industry. Part of the reason for this is the limitations in our current models for developing new molecules. Many years ago, the approach was solely trial and error, which was very costly. With the advent of computers and computational chemistry, our models have improved and we continue to see dramatic improvements in our ability to develop materials that improve lives. Despite this, our models are still built on limited data sets. Machine learning techniques and algorithms show considerable promise for improving such models and allowing us to not only predict necessary properties important for a specific application, but also to account for secondary and tertiary effects that lead to problematic outcomes attributed to certain molecules. While this is an ongoing avenue of general research, one of the bottlenecks to its implementation is a valid, comprehensive, feature sets that describe molecules of interest. When developing new molecules, chemists generally think modularly, focusing on varying only specific fragments (functional groups, substrates, reaction centres). Over the years, descriptors for these fragments have been developed and employed to understand why such changes result in specific effects. Unfortunately, these descriptors do not truly describe the properties of the fragment, only their effect under specific conditions. This research aims to improve upon these models by first developing a set of fragment-based descriptors that are truly representative of its properties. Our focus is on developing such descriptors using the topological properties of the electron density as it has been shown that they are capable of demarcating any molecule into such chemically relevant fragments, and with the overall molecular property being a simple sum of the individual fragments. Three types of fragment properties are considered here: (1) those associated with a molecular graph, which help identify various bonding interactions; (2) those atomic properties obtained by integrating over the atomic basin as defined by the zero-flux surfaces of the molecular graph; and (3) the atomic critical points as obtained from a topological analysis of the laplacian. By combining these into an overall feature set for a large number of fragments, we will provide researchers with a data set for machine learning applications. Following the development of these feature sets, we will apply them in machine learning algorithms. The nature of our approach lends itself to using graph neural networks with vertices augmented by atomic properties and edges with molecular graph properties. This work will lead to an explanatory and predictive model for designing new molecules, hopefully resolving the secondary effect issue.

化学的目的是开发改善我们世界的新分子和材料。尽管我们在世界上看到了显着的改善，但仍有一些非常麻烦的问题导致公众不信任化学工业。造成这种情况的部分原因是我们当前模型中开发新分子的局限性。许多年前，这种方法完全是反复试验，这是非常昂贵的。随着计算机和计算化学的出现，我们的模型有所改善，我们继续看到开发改善生命的材料的能力的显着改善。尽管如此，我们的模型仍基于有限的数据集。机器学习技术和算法在改善此类模型并允许我们预测对特定应用重要的必要特性方面表现出巨大的希望，还可以考虑导致某些分子归因的有问题结果的二级和第三级效应。尽管这是一项持续的一般研究途径，但其实施的瓶颈之一是一个有效，全面的功能集，描述了感兴趣的分子。在开发新分子时，化学家通常会模块化思考，专注于仅改变特定片段（官能团，底物，反应中心）。多年来，已经开发并采用了这些片段的描述符，以了解为什么这种变化会导致特定效果。不幸的是，这些描述符并不能真正描述片段的特性，而只能在特定条件下效应。这项研究旨在通过首先开发一组真正代表其性质的基于碎片的描述符来改进这些模型。我们的重点是使用电子密度的拓扑特性来开发此类描述符，因为它已经表明它们能够将任何分子划分为这种化学相关的片段，并且整体分子特性是单个片段的简单总和。这里考虑了三种类型的片段特性：（1）与分子图相关的那些片段，这些片段有助于识别各种键合相互作用； （2）通过在分子图的零频表面定义的原子盆地上积分获得的原子特性； （3）从laplacian的拓扑分析获得的原子临界点。通过将它们合并为用于大量片段的整体功能集，我们将为研究人员提供用于机器学习应用程序的数据集。在开发这些功能集之后，我们将将它们应用于机器学习算法。我们方法的性质使自己使用图形神经网络，其顶点具有由原子特性和具有分子图特性的边缘增强的。这项工作将导致设计新分子的解释性和预测模型，希望解决次要效应问题。