masashitsubaki

Advanced Industrial Science and Technology (AIST)Japan

Pinned Repositories

CPI_prediction
This is a code for compound-protein interaction (CPI) prediction based on a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.
Language:Python157 6 1936
mbtr-dnn
Deep neural network on many-body tensor representation in PyTorch
10
molecularGNN_3Dstructure
Graph neural network (GNN) for molecular property prediction (3D structure)
Language:Python90 5 122
molecularGNN_smiles
The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius subgraphs (i.e., fingerprints) in molecules.
Language:Python292 6 776
QuantumDeepField_crystal
Quantum deep field for crystal
21
QuantumDeepField_molecule
Quantum deep field for molecule
Language:Python198 4 442

masashitsubaki/molecularGNN_smiles
The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius subgraphs (i.e., fingerprints) in molecules.
Language:Python292 6 776
masashitsubaki/QuantumDeepField_molecule
Quantum deep field for molecule
Language:Python198 4 442
masashitsubaki/CPI_prediction
This is a code for compound-protein interaction (CPI) prediction based on a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins.
Language:Python157 6 1936
masashitsubaki/molecularGNN_3Dstructure
Graph neural network (GNN) for molecular property prediction (3D structure)
Language:Python90 5 122
masashitsubaki/QuantumDeepField_crystal
Quantum deep field for crystal
21
masashitsubaki/mbtr-dnn
Deep neural network on many-body tensor representation in PyTorch
10