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.

化学的目标是开发新的分子和材料来改善我们的世界。虽然我们看到我们的世界有了显著的改善，但也出现了一些非常麻烦的问题，导致公众不信任化学工业。部分原因是我们目前开发新分子的模型存在局限性。许多年前，这种方法完全是反复试验，代价非常高昂。随着计算机和计算化学的出现，我们的模型得到了改进，我们继续看到我们开发改善生活的材料的能力有了戏剧性的改进。尽管如此，我们的模型仍然建立在有限的数据集上。机器学习技术和算法在改进此类模型方面显示出相当大的前景，使我们不仅能够预测对特定应用重要的必要性质，而且还能够解释导致某些分子导致问题结果的二次和三次效应。虽然这是一种正在进行的一般研究途径，但其实施的瓶颈之一是描述感兴趣分子的有效、全面的特征集。当开发新的分子时，化学家通常以模块化的方式思考，只专注于改变特定的片段(官能团、底物、反应中心)。多年来，已经开发和使用了这些片段的描述符来理解为什么这些变化会导致特定的影响。遗憾的是，这些描述符并没有真正描述片段的属性，而只是描述了它们在特定条件下的效果。这项研究旨在通过首先开发一组真正代表其属性的基于片段的描述符来改进这些模型。我们的重点是利用电子密度的拓扑性质来开发这样的描述符，因为已经证明，它们能够将任何分子划分成化学上相关的片段，并且总体分子属性是单个片段的简单和。这里考虑了三种类型的碎片属性：(1)与分子图相关联的那些有助于识别各种成键相互作用的碎片属性；(2)通过在分子图的零通量表面所定义的原子盆上进行积分而获得的原子属性；以及(3)从拉普拉斯拓扑分析获得的原子临界点。通过将这些结合到大量片段的整体特征集中，我们将为研究人员提供机器学习应用的数据集。随着这些特征集的发展，我们将把它们应用到机器学习算法中。我们方法的性质适合使用图神经网络，该网络的顶点由原子属性扩充，边具有分子图属性。这项工作将导致一个解释性和预测性的模型来设计新的分子，有望解决二次效应问题。