---
title: MolGen-large-opt
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/MolGen-large-opt"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/MolGen-large-opt.md"
repository: AI-ModelScope/MolGen-large-opt
last_updated: 2023-10-20
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - bart
architectures:
  - BartForConditionalGeneration
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 28
stars: 0
tags:
  - "molecular language model"
  - SELFIES
  - "molecule optimization"
---

# MolGen-large-opt

> MolGen-large-opt - AI-ModelScope 在 ModelScope 开源的模型。MolGen-large-opt MolGen-large-opt was introduced in the paper "Domain-Agnostic Molecular Generation with Self-feedback" and first released in this repository.

- **Repository**: AI-ModelScope/MolGen-large-opt
- **Tasks**: text-generation
- **Tags**: molecular language model, SELFIES, molecule optimization
- **Downloads**: 28
- **Stars**: 0
- **Last updated**: 2023-10-20

Source: https://www.modelscope.cn/models/AI-ModelScope/MolGen-large-opt

---

# MolGen-large-opt
MolGen-large-opt 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). 

## Model description
MolGen-large-opt is the fine-tuned version of [MolGen-large](https://huggingface.co/zjunlp/MolGen-large). 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-opt can generate molecules with desired properties, making it a valuable tool for molecular optimization.

![image.png](./molgen.png)

## Intended uses
You can use the fine-tuned model for molecule optimization for downstream tasks. See the [repository](https://github.com/zjunlp/MolGen) to look for fine-tune details on a task that interests you.

### How to use
Molecule optimization example:
```python
from modelscope import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("AI-ModelScope/MolGen-large-opt")
model = AutoModelForSeq2SeqLM.from_pretrained("AI-ModelScope/MolGen-large-opt")

sf_input = tokenizer("[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]", return_tensors="pt")
# beam search
molecules = model.generate(input_ids=sf_input["input_ids"],
                              attention_mask=sf_input["attention_mask"],
                              max_length=35,
                              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]
['[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]',
'[N][#C][C][C][C@@H1][C][C][C][C][C][C][C][C][C][C][C][C][C][C][Ring1][N][=O]']
```


### BibTeX entry and citation info
```bibtex
@article{fang2023molecular,
  title={Molecular Language Model as Multi-task Generator},
  author={Fang, Yin and Zhang, Ningyu and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
  journal={arXiv preprint arXiv:2301.11259},
  year={2023}
}
```