BERT base for SMILES
This is bidirectional transformer pretrained on SMILES (simplified molecular-input line-entry system) strings.
Example: Amoxicillin
O=C([C@@H](c1ccc(cc1)O)N)N[C@@H]1C(=O)N2[C@@H]1SC([C@@H]2C(=O)O)(C)C
Two training objectives were used:
- masked language modeling
- molecular-formula validity prediction
Intended uses
This model is primarily aimed at being fine-tuned on the following tasks:
- molecule classification
- molecule-to-gene-expression mapping
- cell targeting
How to use in your code
from transformers import BertTokenizerFast, BertModel
checkpoint = 'unikei/bert-base-smiles'
tokenizer = BertTokenizerFast.from_pretrained(checkpoint)
model = BertModel.from_pretrained(checkpoint)
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'
tokens = tokenizer(example, return_tensors='pt')
predictions = model(**tokens)
Research
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Jouary et al. (2025) Bridging scales between chemical space and behavioral phenotype:
A cross-modal mapping between behavior and molecular structure, derived using the unikei/bert-base-smiles model, effectively distinguished between distinct neurotransmitter classes, such as dopaminergic/serotonergic ligands, purines, and metabotropic glutamate ligands.