Work

Comparative Analysis of PharmHGT, GCN, and GAT Models for Predicting LogCMC in Surfactants

Year: 2025

Type: conference-paper

Abstract: Predicting the critical micelle concentration (CMC) of surfactants is essential for optimizing their applications in various industries, including pharmaceuticals, detergents, and emulsions. In this study, we investigate the performance of graph-based machine learning models, specifically Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and a graph-transformer model, PharmHGT, for predicting CMC values. We aim to determine the most effective model for capturing the structural and physicochemical properties of surfactants. Our results provide insights into the relative strengths of each approach, highlighting the potential advantages of transformer-based architectures like PharmHGT in handling molecular graph representations compared to traditional graph neural networks. This comparative study serves as a step towards enhancing the accuracy of CMC predictions, contributing to the efficient design of surfactants for targeted applications.

Source: Systems and Control Transactions

DOI: 10.69997/sct.107030

Authors: Gabriela C. Theis Marchan, Teslim Olayiwola, Jose Romagnoli

Institution: Louisiana State University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: U.S. Department of Energy, Office of Science

Award: BRIDGING THE LENGTH SCALES IN IONIC SEPARATIONS VIA DATA-DRIVEN MACHINE LEARNING