A Granularity-level Information Fusion Strategy on Hypergraph Transformer for Predicting Synergistic Effects of Anticancer Drugs
HypertranSynergy is a hypergraph learning network model used to predict the synergistic effect of anticancer drugs. It mainly includes two parts: CIE is a coarse-grained information extraction module based on hypergraph transformer, and FIE is a fine-grained information extraction module based on attention network. By coupling the two parts into HypertranSynergy, you can make the embeddings contain rich grain-level information to make prediction.
- Python 3.7 or higher
- PyTorch 1.8.0 or higher
- deepchem 2.5.0
- rdkit 2022.9.5
- torch-geometric 2.1.0
- Running the "main_.py" for the classification task.
- Running the "main_reg.py" for the regression task.
Thanks the code from Hts