Mirrored by Aurigene AI
Discovery stage: Evidence and literature / Lead optimization
Writes a natural-language description of a molecule from its SMILES. Useful for annotating screening hits and summarising libraries.
Upstream:
laituan245/molt5-large-smiles2caption- all credit to the original authors; the model card and licence below are theirs.Explore the rest of the catalogue: Molecule Explorer - Protein Target Explorer - Drug Discovery Model Hub
This model can be used to generate an input caption from a SMILES string.
Example Usage
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large-smiles2caption')
input_text = 'C1=CC2=C(C(=C1)[O-])NC(=CC2=O)C(=O)O'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Paper
For more information, please take a look at our paper.
Paper: Translation between Molecules and Natural Language
Authors: Carl Edwards*, Tuan Lai*, Kevin Ros, Garrett Honke, Heng Ji