gninatorch.gnina module
- gninatorch.gnina.load_gnina_model(gnina_model: str, dimension: float = 23.5, resolution: float = 0.5)[source]
Load GNINA model.
- Parameters
gnina_model (str) – GNINA model name
dimension (float) – Grid dimension (in Angstrom)
resolution (float) – Grid resolution (in Angstrom)
- gninatorch.gnina.load_gnina_models(model_names: Iterable[str], dimension: float = 23.5, resolution: float = 0.5)[source]
Load GNINA models.
- Parameters
model_names (Iterable[str]) – List of GNINA model names
- gninatorch.gnina.main(args)[source]
Run inference with GNINA pre-trained models.
- Parameters
args (Namespace) – Parsed command line arguments
Notes
Models are used in evaluation mode, which is essential for the dense models since they use batch normalisation.
- gninatorch.gnina.options(args: Optional[List[str]] = None)[source]
Define options and parse arguments.
- Parameters
args (Optional[List[str]]) – List of command line arguments
- gninatorch.gnina.setup_gnina_model(cnn: str = 'default', dimension: float = 23.5, resolution: float = 0.5) Union[Module, bool] [source]
Load model or ensemble of models.
- Parameters
cnn (str) – CNN model name
dimension (float) – Grid dimension
resolution (float) – Grid resolution
- Returns
Model or ensemble of models
- Return type
nn.Module
Notes
Mimicks GNINA CLI. The model is returned in evaluation mode. This is essential to use the dense model correctly (due to the
nn.BatchNorm
layers).