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--- |
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tags: |
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- molecular language model |
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- SELFIES |
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- molecule generation |
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widget: |
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- text: '[C][=C][C][=C][C][=C][Ring1][=Branch1]' |
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inference: false |
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--- |
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# MolGen-large |
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MolGen-large was introduced in the paper ["Domain-Agnostic Molecular Generation with Self-feedback"](https://arxiv.org/pdf/2301.11259.pdf) and first released in [this repository](https://github.com/zjunlp/MolGen). It is a pre-trained molecular generative model built using the 100\% robust molecular language representation, SELFIES. |
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## Model description |
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MolGen-large is the first pre-trained model that only produces chemically valid molecules. |
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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. |
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Specifically, MolGen-large employs a bidirectional Transformer as its encoder and an autoregressive Transformer as its decoder. |
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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. |
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## Intended uses |
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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](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you. |
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### How to use |
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Molecule generation example: |
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```python |
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large") |
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large") |
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>>> sf_input = tokenizer("[C][=C][C][=C][C][=C][Ring1][=Branch1]", return_tensors="pt") |
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>>> # beam search |
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>>> molecules = model.generate(input_ids=sf_input["input_ids"], |
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attention_mask=sf_input["attention_mask"], |
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max_length=15, |
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min_length=5, |
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num_return_sequences=5, |
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num_beams=5) |
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>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules] |
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['[C][=C][C][=C][C][=C][Ring1][=Branch1]', |
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'[C][=C][C][=C][C][=C][C][=C][Ring1][=Branch1]', |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1][C][=C][C][=C]', |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][C][=C][C]', |
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'[C][=C][C][=C][C][=C][Ring1][=Branch1][C@H1][=C][C][=C]'] |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{fang2023domain, |
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author = {Yin Fang and |
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Ningyu Zhang and |
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Zhuo Chen and |
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Xiaohui Fan and |
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Huajun Chen}, |
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title = {Domain-Agnostic Molecular Generation with Chemical Feedback}, |
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booktitle = {{ICLR}}, |
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publisher = {OpenReview.net}, |
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year = {2024}, |
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url = {https://openreview.net/pdf?id=9rPyHyjfwP} |
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} |
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``` |
