dmlc--dgl
Mufei Li
67 行
1.8 KiB
ReStructuredText
67 行
1.8 KiB
ReStructuredText
.. _apimodelpretrain:
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Pre-trained Models
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==================
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We provide multiple pre-trained models for users to use without the need of training from scratch.
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Example Usage
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-------------
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Property Prediction
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```````````````````
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.. code-block:: python
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from dgllife.data import Tox21
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from dgllife.model import load_pretrained
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from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
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dataset = Tox21(smiles_to_bigraph, CanonicalAtomFeaturizer())
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model = load_pretrained('GCN_Tox21') # Pretrained model loaded
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model.eval()
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smiles, g, label, mask = dataset[0]
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feats = g.ndata.pop('h')
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label_pred = model(g, feats)
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print(smiles) # CCOc1ccc2nc(S(N)(=O)=O)sc2c1
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print(label_pred[:, mask != 0]) # Mask non-existing labels
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# tensor([[ 1.4190, -0.1820, 1.2974, 1.4416, 0.6914,
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# 2.0957, 0.5919, 0.7715, 1.7273, 0.2070]])
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Generative Models
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.. code-block:: python
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from dgllife.model import load_pretrained
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model = load_pretrained('DGMG_ZINC_canonical')
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model.eval()
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smiles = []
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for i in range(4):
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smiles.append(model(rdkit_mol=True))
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print(smiles)
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# ['CC1CCC2C(CCC3C2C(NC2=CC(Cl)=CC=C2N)S3(=O)=O)O1',
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# 'O=C1SC2N=CN=C(NC(SC3=CC=CC=N3)C1=CC=CO)C=2C1=CCCC1',
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# 'CC1C=CC(=CC=1)C(=O)NN=C(C)C1=CC=CC2=CC=CC=C21',
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# 'CCN(CC1=CC=CC=C1F)CC1CCCN(C)C1']
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If you are running the code block above in Jupyter notebook, you can also visualize the molecules generated with
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.. code-block:: python
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from IPython.display import SVG
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from rdkit import Chem
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from rdkit.Chem import Draw
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mols = [Chem.MolFromSmiles(s) for s in smiles]
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SVG(Draw.MolsToGridImage(mols, molsPerRow=4, subImgSize=(180, 150), useSVG=True))
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.. image:: https://data.dgl.ai/dgllife/dgmg/dgmg_model_zoo_example2.png
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API
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---
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.. autofunction:: dgllife.model.load_pretrained
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