Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction | ScienceToStartup | ScienceToStartup

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References (58)

[1]

Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck

2024Van Thuy Hoang, O-Joun Lee

[2]

Expressivity and Generalization: Fragment-Biases for Molecular GNNs

2024Tom Wollschlager, Niklas Kemper et al.

[3]

Learning and Leveraging World Models in Visual Representation Learning

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"GraSPNet leverages graph self-supervised learning to provide chemically meaningful molecular representations that outperform existing methods."

Molecular Representation Learning•Score: 4•View PDF ↗

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Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction

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References (58)

Founder's Pitch

"GraSPNet leverages graph self-supervised learning to provide chemically meaningful molecular representations that outperform existing methods."

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Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction

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References (58)

Founder's Pitch

"GraSPNet leverages graph self-supervised learning to provide chemically meaningful molecular representations that outperform existing methods."

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