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--- |
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license: bigscience-openrail-m |
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widget: |
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- text: O=C([C@@H](c1ccc(cc1)O)N)[MASK][C@@H]1C(=O)N2[C@@H]1SC([C@@H]2C(=O)O)(C)C |
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datasets: |
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- ChEMBL |
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pipeline_tag: fill-mask |
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tags: |
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- biology |
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- medical |
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--- |
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# BERT base for SMILES |
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This is bidirectional transformer pretrained on SMILES (simplified molecular-input line-entry system) strings. |
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Example: Amoxicillin |
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``` |
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O=C([C@@H](c1ccc(cc1)O)N)N[C@@H]1C(=O)N2[C@@H]1SC([C@@H]2C(=O)O)(C)C |
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``` |
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Two training objectives were used: |
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1. masked language modeling |
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2. molecular-formula validity prediction |
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## Intended uses |
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This model is primarily aimed at being fine-tuned on the following tasks: |
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- molecule classification |
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- molecule-to-gene-expression mapping |
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- cell targeting |
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## How to use in your code |
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```python |
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from transformers import BertTokenizerFast, BertModel |
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checkpoint = 'unikei/bert-base-smiles' |
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tokenizer = BertTokenizerFast.from_pretrained(checkpoint) |
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model = BertModel.from_pretrained(checkpoint) |
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example = 'O=C([C@@H](c1ccc(cc1)O)N)N[C@@H]1C(=O)N2[C@@H]1SC([C@@H]2C(=O)O)(C)C' |
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tokens = tokenizer(example, return_tensors='pt') |
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predictions = model(**tokens) |
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``` |
