DeepChem officially supports Python 3.8 through 3.10 and requires these packages on any condition.
DeepChem has a number of "soft" requirements.
Package name | Version | Location where this package is used (dc: deepchem) |
---|---|---|
BioPython | latest | :code:dc.utlis.genomics_utils |
Deep Graph Library | 0.5.x | dc.feat.graph_data , dc.models.torch_models |
DGL-LifeSci | 0.2.x | dc.models.torch_models |
HuggingFace Transformers | Not Testing | dc.feat.smiles_tokenizer |
HuggingFace Tokenizers | latest | :code:dc.feat.HuggingFaceVocabularyBuilder |
LightGBM | latest | :code:dc.models.gbdt_models |
matminer | latest | :code:dc.feat.materials_featurizers |
MDTraj | latest | :code:dc.utils.pdbqt_utils |
Mol2vec | latest | :code:dc.utils.molecule_featurizers |
Mordred | latest | :code:dc.utils.molecule_featurizers |
NetworkX | latest | :code:dc.utils.rdkit_utils |
OpenAI Gym | Not Testing | dc.rl |
OpenMM | latest | :code:dc.utils.rdkit_utils |
PDBFixer | latest | :code:dc.utils.rdkit_utils |
Pillow | latest | :code:dc.data.data_loader, dc.trans.transformers |
PubChemPy | latest | :code:dc.feat.molecule_featurizers |
pyGPGO | latest | :code:dc.hyper.gaussian_process |
Pymatgen | latest | :code:dc.feat.materials_featurizers |
PyTorch | 2.2.1 | dc.models.torch_models |
PyTorch Geometric | latest (with PyTorch 2.2.1) | dc.feat.graph_data dc.models.torch_models |
RDKit | latest | Many modules (we recommend you to install) |
simdna | latest | :code:dc.metrics.genomic_metrics, dc.molnet.dnasim |
TensorFlow | 2.15 | dc.models deepchem>=2.4.0 depends on TensorFlow v2(2.3.x) deepchem<2.4.0 depends on TensorFlow v1(>=1.14) |
Tensorflow Probability | 0.23.x | :code:dc.rl |
Weights & Biases | Not Testing | dc.models.keras_model , dc.models.callbacks |
XGBoost | latest | :code:dc.models.gbdt_models |
Tensorflow Addons | latest | :code:dc.models.optimizers |
pySCF | latest | :code:dc.models.torch_models.ferminet |
pysam | latest | :code:dc.feat.bio_seq_featurizer dc.models.data_loader |