File size: 3,074 Bytes
75b2631 7298339 9bfe9f0 a0350d3 4553e37 75b2631 58b548a 2479293 63cb0f6 089d787 58b548a 089d787 6ffb3a2 612be02 17d47f2 0fe98b9 089d787 17d47f2 2a45f64 17d47f2 0abfc14 17d47f2 89aaa46 4b74b2f dbfa68a 5d63c09 dbfa68a 8c3e4b8 89aaa46 df1516e 17d47f2 089d787 315af48 317068e 3c6e8fb 315af48 089d787 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
tags:
- molecular language model
- SELFIES
- molecule generation
widget:
- text: '[C][=C][C][=C][C][=C][Ring1][=Branch1]'
inference: false
---
# MolGen-large
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.
## 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](https://github.com/zjunlp/MolGen) 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}
}
``` |