Machine Learning Prediction of Optical Properties of Coumarin Derivatives Using Gaussian-Weighted Graph Convolution and Subgraph Modular Input

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Seokwoo Kim, Minhi Han, Jinyong Park, Kiwoong Lee and Sungnam Park*, 
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Abstract

Coumarin derivatives have been widely developed and utilized as chromophores and fluorophores in various research fields. In this study, we constructed an experimental database of the optical properties─specifically, absorption and emission wavelengths measured in solutions─and developed a machine learning (ML) model based on Gaussian-weighted graph convolution (GWGC) and subgraph modular input (SMI) to predict these properties. The GWGC was introduced as a novel molecular representation that accounts for interatomic effects among neighboring atoms when the optical properties of coumarin derivatives were predicted. The SMI was introduced to represent coumarin derivatives as subgraphs composed of a coumarin core and six substituents, thereby modularizing the molecular vector into a core vector and substituent vectors. This approach encodes both the separate chemical information on the core and substituents as well as the positional information on the substituents, facilitating an understanding of how each substituent influences the optical properties of the coumarin core. ML models leveraging GWGC and SMI outperformed those based on RDKit descriptors and count-based Morgan fingerprint. The ML models with GWGC and SMI can be generally applied to predict properties of molecules composed of a core structure and its various substituents.

Abstract Image

基于高斯加权图卷积和子图模输入的香豆素衍生物光学性质机器学习预测
香豆素衍生物作为发色团和荧光团在各个研究领域得到了广泛的开发和应用。在这项研究中,我们构建了一个光学特性的实验数据库──特别是溶液中测量的吸收和发射波长──并开发了一个基于高斯加权图卷积(GWGC)和子图模块输入(SMI)的机器学习(ML)模型来预测这些特性。在预测香豆素衍生物的光学性质时,引入了GWGC作为一种新的分子表示,用于解释相邻原子之间的相互作用。引入SMI将香豆素衍生物表示为一个香豆素核心和六个取代基组成的子图,从而将分子向量模块化为一个核心向量和取代基向量。这种方法对核心和取代基上的单独化学信息以及取代基上的位置信息进行编码,有助于理解每个取代基如何影响香豆素核心的光学性质。利用GWGC和SMI的ML模型优于基于RDKit描述符和基于计数的Morgan指纹的模型。具有GWGC和SMI的ML模型可广泛用于预测由核心结构及其各种取代基组成的分子的性质。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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