Automatic Creation of Molecular Substructures for Accurate Estimation of Pure Component Properties using Connectivity Matrices
Automatic Creation of Molecular Substructures for Accurate Estimation of Pure Component Properties using Connectivity Matrices
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DOI：
10.1016/j.ces.2022.118214
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发表时间：
2022-10
影响因子：
4.7
通讯作者：
Qiong Pan;Xiaolei Fan;Jie Li
中科院分区：
文献类型：
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作者：
Qiong Pan;Xiaolei Fan;Jie Li
纯组分理化性质的估算在过去几十年中受到了广泛关注，因为它们是化工产品和工艺设计的基础。在这项工作中，我们提出了一个连接矩阵为基础的框架，再加上机器学习自动创建的分子特征，用于准确地估计纯成分的属性。采用连接矩阵的概念来表示分子结构。提出了一种提取策略，从该连接矩阵中自动系统地提取出大量的子矩阵（或分子结构片段），每个子矩阵代表原子/键的环境。这种提取不会导致分子信息的任何损失。子矩阵，然后转换成分子特征的基础上矩阵特征值。频率和皮尔逊相关性分析用于提取关键特征，使用主成分分析进一步减少。机器学习方法，如人工神经网络（ANN）和高斯过程回归（GPR）分别用于开发预测模型。与现有方法相比，所提出的框架的能力和优势，说明通过估计纯化合物的正常沸点。
Estimation of pure component physiochemical properties has received much attention in the last decades as they serve as the basis for design of chemical products and processes. In this work, we propose a connectivity matrix-based framework coupled with machine learning for automatic creation of molecular features used to accurately estimate pure component properties. The concept of connectivity matrix is employed to represent a molecule structure. An extraction strategy is proposed to extract a plethora of submatrices (or molecular structural fragments) with each representing the environment of an atom/bond automatically and systematically from this connectivity matrix. This extraction does not cause any loss of molecular information. The submatrices are then transferred into molecular features based on matrix eigenvalues. Frequency and Pearson correlation analysis are used to extract key features, which are further reduced using principal component analysis. Machine-learning methods such as the artificial neural network (ANN) and Gaussian process regression (GPR) are used to develop prediction models, respectively. The capability and advantages of the proposed framework in comparison to existing methods are illustrated through estimation of normal boiling point of pure compounds.