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---
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license: mit
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---
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## MorganGen
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To use it you can clone the HF gitrepo before running the following examples:
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```commandline
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git lfs install # Only once if not done already
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git clone https://huggingface.co/lamthuy/MorganGen
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```
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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.
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```
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s = [12][184][1200]
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```
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represents a fingerprint where only bits 12, 184, and 1200 are set to 1, and the remaining bits are 0.
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# Running example
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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.
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```python
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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from utils import MorganFingerprint, morgan_fingerprint_to_text
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# Load the checkpoint and the tokenizer
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checkpoint_path = "lamthuy/MorganGen"
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path)
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tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
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# Given a SMILES, get its fingerpint
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smiles = "CC(=O)OC1=CC=CC=C1C(=O)O"
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m = MorganFingerprint()
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mf = m.smiles_to_morgan(smiles)
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# convert it to the indices text format
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s = morgan_fingerprint_to_text(mf)
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# encode
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input_ids = tokenizer.encode(s, return_tensors="pt")
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# Generate output sequence
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output_ids = model.generate(input_ids, max_length=64, num_beams=5)
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# Decode the generated output
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output_smiles = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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print(output_smiles)
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```
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# Reference
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```
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@inproceedings{hoang2024morgangen,
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title={MorganGen: Generative Modeling of SMILES Using Morgan Fingerprint Features},
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author={Hoang, Lam Thanh and D{\'\i}az, Ra{\'u}l Fern{\'a}ndez and Lopez, Vanessa},
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booktitle={American Chemical Society (ACS) Fall Meeting},
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year={2024}
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}
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```
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