You can deploy this model and start earning money today!

zjunlp/
MolGen-large

Name: MolGen-large
Rating: 2.5 (680 reviews)

molecular language model

SELFIES

molecule generation

2301.11259

Views

No views yet

MolGen-large

MolGen-large was introduced in the paper "Domain-Agnostic Molecular Generation with Self-feedback" and first released in this repository. It is a pre-trained molecular generative model built using the 100% robust molecular language representation, SELFIES.

Model description

MolGen-large is the first pre-trained model that only produces chemically valid molecules. With a training corpus of over 100 million molecules in SELFIES representation, MolGen-large learns the intrinsic structural patterns of molecules by mapping corrupted SELFIES to their original forms. Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder. Through its carefully designed multi-task molecular prefix tuning (MPT), MolGen-large can generate molecules with desired properties, making it a valuable tool for molecular optimization.

Intended uses

You can use the raw model for molecule generation or fine-tune it to a downstream task. Please take note that the following examples only demonstrate the utilization of our pre-trained model for molecule generation. See the repository to look for fine-tune details on a task that interests you.

How to use

Molecule generation example:

python

>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large")

>>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt")
>>> # beam search
>>> molecules = model.generate(input_ids=sf_input["input_ids"],
                              attention_mask=sf_input["attention_mask"],
                              max_length=15,
                              min_length=5,
                              num_return_sequences=5,
                              num_beams=5)
>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
['[C][=C][C][=C][C][=C][Ring1][=Branch1]',
'[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C]',
'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C]']

BibTeX entry and citation info

bibtex

@inproceedings{fang2023domain,
  author       = {Yin Fang and
                  Ningyu Zhang and
                  Zhuo Chen and
                  Xiaohui Fan and
                  Huajun Chen},
  title        = {Domain-Agnostic Molecular Generation with Chemical Feedback},
  booktitle    = {{ICLR}},
  publisher    = {OpenReview.net},
  year         = {2024},
  url          = {https://openreview.net/pdf?id=9rPyHyjfwP}
}