DeepDDS¶
- class DeepDDS(*, context_channels, context_hidden_dims=None, drug_channels=66, drug_gcn_hidden_dims=None, drug_mlp_hidden_dims=None, context_output_size=32, fc_hidden_dims=None, dropout=0.5)[source]¶
Bases:
chemicalx.models.base.Model
An implementation of the DeepDDS model from [wang2021].
This implementation follows the code on github where the paper and the code diverge. https://github.com/Sinwang404/DeepDDs/tree/master
See also
This model was suggested in https://github.com/AstraZeneca/chemicalx/issues/19
- wang2021
Wang, J., et al. (2021). DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. arXiv, 2107.02467.
Methods Summary
forward
(context_features, molecules_left, …)Run a forward pass of the DeeDDS model.
unpack
(batch)Return the context features, left drug features and right drug features.
Methods Documentation
- forward(context_features, molecules_left, molecules_right)[source]¶
Run a forward pass of the DeeDDS model.
- Parameters
context_features (
FloatTensor
) – A matrix of cell line featuresmolecules_left (
PackedGraph
) – A matrix of left drug featuresmolecules_right (
PackedGraph
) – A matrix of right drug features
- Return type
FloatTensor
- Returns
A vector of predicted synergy scores