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Exploring data augmentation: Multi-task methods for molecular property prediction
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DOI：10.1016/j.compchemeng.2025.109253.png)
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Abstract
En 中文
• Predicting molecular properties with multi-task graph neural networks. • Explored different types of molecular property data augmentation. • Analyzed data augmentation in practical scenarios of molecular data availability. • Recommendations for training of graph neural networks.
Keywords:
Multi-task learning
Molecular property prediction
Graph neural networks
Missing data
Machine learning
Journal
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3.9
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8.1K
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Cited Papers
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
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