Search Results for author:
Found 2 papers, 1 papers with code
An effective self-supervised framework for learning expressive molecular global representations to drug discovery
In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level.
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
