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Found 4 papers, 1 papers with code
Neural Embeddings for Protein Graphs
Proteins perform much of the work in living organisms, and consequently the development of efficient computational methods for protein representation is essential for advancing large-scale biological research.
CIN++: Enhancing Topological Message Passing
Our message passing scheme accounts for the aforementioned limitations by letting the cells to receive also lower messages within each layer.
Ranked #1 on
Graph Classification
on HIV dataset
Deep reinforcement learning for quantum multiparameter estimation
Estimation of physical quantities is at the core of most scientific research and the use of quantum devices promises to enhance its performances.
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
To achieve such skills, it is important to develop good representations of chemical reactions, or good deep learning architectures that can learn such representations automatically from the data.
