VAE-Assisted Data Augmentation for Improved Molecular Prediction with Graph Neural Networks (GNNs) in Low-Data Regimes

This study presents a novel approach to enhancing molecular property prediction through variational autoencoder (VAE)-assisted data augmentation in low-data regimes. The methodology combines graph neural networks (GNNs) with VAEs to improve predictive accuracy on molecular datasets from MoleculeNet,...

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Bibliographic Details
Main Authors: Gabriela C. Theis Marchan, Pegah Naghshnejad, Andrew Okafor, Jose A. Romagnoli
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2025-07-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/15421
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