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。虽然我们已经看到我们的世界有了显著的改善，但也有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，这种方法完全是试错法，成本非常高。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得到了显着提高。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，不仅使我们能够预测对特定应用至关重要的必要属性，而且还可以解释导致某些分子产生问题结果的二级和三级效应。虽然这是一个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。在开发新分子时，化学家通常以模块化的方式思考，只关注特定片段（官能团、底物、反应中心）的变化。多年来，这些片段的描述符已经开发出来，并用于理解为什么这种变化会导致特定的影响。不幸的是，这些描述符并不能真正描述碎片的性质，只能描述它们在特定条件下的作用。本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。我们的重点是开发这样的描述符使用的电子密度的拓扑特性，因为它已经被证明，他们是能够划定任何分子到这样的化学相关的片段，并与整体分子特性是一个简单的总和的个别片段。这里考虑了三种类型的碎片性质：（1）与分子图相关的性质，这有助于识别各种成键相互作用;（2）通过在分子图的零通量表面定义的原子盆上积分获得的原子性质;以及（3）从拉普拉斯算子的拓扑分析获得的原子临界点。通过将这些组合成大量片段的整体特征集，我们将为研究人员提供用于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。我们的方法的性质适合于使用图神经网络，其顶点由原子属性增强，边缘具有分子图属性。这项工作将为设计新分子提供一个解释性和预测性的模型，希望能解决次级效应问题。