Lee, Sanghoon Kim, Hyun Woo 2025-08-31T03:30:27Z 2025-08-31T03:30:27Z 2025-08-27 2025-12 2513-0390 https://pubs.kist.re.kr/handle/201004/153083 Molecular structures that can be readily represented by graphs comprising constituent atoms (nodes) and their chemical bonds (edges) can also be used as input data for well-known machine learning (ML) models that process this data, such as graph neural networks (GNNs). GNNs have shown a reasonable performance in the predicting properties of chemical systems. In typical applications of GNNs to chemistry-related fields, the main objective is to create an optimal molecular representation by aggregating atomic features and pooling features in the graph. In this study, two different approaches are investigated that can possibly generate better molecular representations. First, intermolecular edges are created to predict the photochemical properties of chromophore molecules in the solution. These intermolecular edges are constructed using atomic partial charges, inspired from the fact that electrostatic interaction is the main component of solute-solvent interaction. In the second approach, the effect of the aggregation and pooling functions is investigated. The results show that intermolecular electrostatic edges based on ground state charges prevent the GNN model from generating more effective molecular representations. On the contrary, the model demonstrated better performance when the averaging and adding operations are employed in a hybrid manner for the aggregation and pooling functions. English WILEY-V C H VERLAG GMBH Revealing the Impact of Aggregations in the Graph-Based Molecular Machine Learning: Electrostatic Interaction Versus Pooling Methods Article 10.1002/adts.202500133 1 Advanced Theory and Simulations, v.8, no.12 Advanced Theory and Simulations 8 12 N scie scopus 001547748000001 2-s2.0-105012986767 Multidisciplinary Sciences Science & Technology - Other Topics Article PREDICTION aggregation graph neural network machine learning pooling