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lamthuy/
MorganGen

safetensors

bart

mit

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MorganGen

To use it you can clone the HF gitrepo before running the following examples:

commandline

git lfs install  # Only once if not done already
git clone https://huggingface.co/lamthuy/MorganGen

A generative model trained on 120 million SMILES strings from the ZINC database. The model takes as input a sequence of indices representing the active bits in a 2048-bit Morgan fingerprint. Each index corresponds to a bit set to 1, while all other bits are 0.

s = [12][184][1200]

represents a fingerprint where only bits 12, 184, and 1200 are set to 1, and the remaining bits are 0.

Running example

The following code snippet in the notebook demonstrates how to load the model from a checkpoint and generate a new SMILES string, conditioned on a given input SMILES.

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import MorganFingerprint, morgan_fingerprint_to_text


# Load the checkpoint and the tokenizer
checkpoint_path = "lamthuy/MorganGen"
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)

# Given a SMILES, get its fingerpint
smiles = "CC(=O)OC1=CC=CC=C1C(=O)O"
m = MorganFingerprint()
mf = m.smiles_to_morgan(smiles)

# convert it to the indices text format
s = morgan_fingerprint_to_text(mf)

# encode
input_ids = tokenizer.encode(s, return_tensors="pt")
# Generate output sequence
output_ids = model.generate(input_ids, max_length=64, num_beams=5)

# Decode the generated output
output_smiles = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_smiles)

Reference

@inproceedings{hoang2024morgangen,
  title={MorganGen: Generative Modeling of SMILES Using Morgan Fingerprint Features},
  author={Hoang, Lam Thanh and D{\'\i}az, Ra{\'u}l Fern{\'a}ndez and Lopez, Vanessa},
  booktitle={American Chemical Society (ACS) Fall Meeting},
  year={2024}
}