Graph Signal Processing Approach to QSAR/QSPR Model Learning of Compounds
Graph Signal Processing Approach to QSAR/QSPR Model Learning of Compounds
复制标题
化合物 QSAR/QSPR 模型学习的图信号处理方法
DOI：
10.1109/tpami.2020.3032718
复制
发表时间：
2022-04-01
影响因子：
23.6
通讯作者：
Zhang, Jingxin
中科院分区：
文献类型：
--
作者：
Song, Xiaoying;Chai, Li;Zhang, Jingxin
在化学应用中，化合物的活性/性质与结构之间的定量关系是至关重要的。为了了解这种定量关系，人们设计了数百种分子描述符来描述结构，主要是基于分子图的顶点和边的性质。然而，对于具有相同分子图的不同化合物，许多描述符退化到相同的值，导致模型失效。在本文中，我们设计了一个多维信号的分子图的每个顶点，以获得新的描述符具有更高的区分度。我们把新的和传统的描述符作为从描述符数据学习的描述符图上的信号，并使用从描述符图导出的拉普拉斯滤波器来增强描述符的相异性。结合这些模型学习技术，我们提出了一种基于图形信号处理的方法，以获得可靠的新模型，用于学习定量关系和预测化合物的性质。我们还提供了沸点模型的化学见解。实验结果证明了该方法的有效性和优越性。
Quantitative relationship between the activity/property and the structure of compound is critical in chemical applications. To learn this quantitative relationship, hundreds of molecular descriptors have been designed to describe the structure, mainly based on the properties of vertices and edges of molecular graph. However, many descriptors degenerate to the same values for different compounds with the same molecular graph, resulting in model failure. In this paper, we design a multidimensional signal for each vertex of the molecular graph to derive new descriptors with higher discriminability. We treat the new and traditional descriptors as the signals on the descriptor graph learned from the descriptor data, and enhance descriptor dissimilarity using the Laplacian filter derived from the descriptor graph. Combining these with model learning techniques, we propose a graph signal processing based approach to obtain reliable new models for learning the quantitative relationship and predicting the properties of compounds. We also provide insights from chemistry for the boiling point model. Several experiments are presented to demonstrate the validity, effectiveness and advantages of the proposed approach.