Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
| """ML baseline models for DTI binary prediction. | |
| Requires torch (install with `pip install negbiodb[ml]`). | |
| """ | |
| __all__ = ["DeepDTA", "GraphDTA", "DrugBAN"] | |
| def __getattr__(name: str): | |
| if name in ("DeepDTA", "GraphDTA", "DrugBAN"): | |
| try: | |
| import torch # noqa: F401 | |
| except ImportError as e: | |
| raise ImportError( | |
| f"negbiodb.models.{name} requires torch. " | |
| "Install with: pip install negbiodb[ml]" | |
| ) from e | |
| if name == "DeepDTA": | |
| from negbiodb.models.deepdta import DeepDTA | |
| return DeepDTA | |
| if name == "GraphDTA": | |
| from negbiodb.models.graphdta import GraphDTA | |
| return GraphDTA | |
| if name == "DrugBAN": | |
| from negbiodb.models.drugban import DrugBAN | |
| return DrugBAN | |
| raise AttributeError(f"module 'negbiodb.models' has no attribute {name!r}") | |