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https://github.com/zjunlp/molgen

[ICLR 2024] Domain-Agnostic Molecular Generation with Chemical Feedback
https://github.com/zjunlp/molgen

generation huggingface iclr2024 language-model molecular-generation molecular-optimization molecule molgen multitask pre-trained-language-models pre-trained-model pre-training pytorch selfies targeted-molecular-generation

Last synced: 26 days ago
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[ICLR 2024] Domain-Agnostic Molecular Generation with Chemical Feedback

Host: GitHub
URL: https://github.com/zjunlp/molgen
Owner: zjunlp
License: mit
Created: 2023-01-04T02:38:28.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-05-13T14:24:51.000Z (6 months ago)
Last Synced: 2024-10-09T22:06:31.292Z (26 days ago)
Topics: generation, huggingface, iclr2024, language-model, molecular-generation, molecular-optimization, molecule, molgen, multitask, pre-trained-language-models, pre-trained-model, pre-training, pytorch, selfies, targeted-molecular-generation
Language: Python
Homepage: https://huggingface.co/spaces/zjunlp/MolGen
Size: 16.4 MB
Stars: 124
Watchers: 7
Forks: 9
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

⚗️ MolGen

Domain-Agnostic Molecular Generation with Chemical Feedback

📃 Paper • 🤗 Model • 🔬 Space

[![Pytorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?e&logo=PyTorch&logoColor=white)](https://pytorch.org/)
![](https://img.shields.io/badge/version-1.0.1-blue)
[![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/zjunlp/MolGen/blob/main/LICENSE)

# 🔔 News

- **`2024-2` We've released [ChatCell](https://huggingface.co/papers/2402.08303), a new paradigm that leverages natural language to make single-cell analysis more accessible and intuitive. Please visit our [homepage](https://www.zjukg.org/project/ChatCell) and [Github page](https://github.com/zjunlp/ChatCell) for more information.**
- **`2024-1` Our paper [Domain-Agnostic Molecular Generation with Chemical Feedback](https://github.com/zjunlp/MolGen) is accepted by ICLR 2024.**
- **`2024-1` Our paper [Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models](https://github.com/zjunlp/Mol-Instructions) is accepted by ICLR 2024.**
- **`2023-10` We open-source [MolGen-7b](https://huggingface.co/zjunlp/MolGen-7b), which now supports de novo molecule generation!**
- **`2023-6` We open-source [KnowLM](https://github.com/zjunlp/KnowLM), a knowledgeable LLM framework with pre-training and instruction fine-tuning code (supports multi-machine multi-GPU setup).**
- **`2023-6` We release [Mol-Instructions](https://github.com/zjunlp/Mol-Instructions), a large-scale biomolecule instruction dataset for large language models.**
- **`2023-5` We propose [Knowledge graph-enhanced molecular contrAstive learning with fuNctional prOmpt (KANO)](https://github.com/HICAI-ZJU/KANO) on `Nature Machine Intelligence`, exploiting fundamental domain knowledge in both pre-training and fine-tuning.**
- **`2023-4` We provide a NLP for science paper-list at [https://github.com/zjunlp/NLP4Science_Papers](https://github.com/zjunlp/NLP4Science_Papers).**
- **`2023-3` We release our pre-trained and fine-tuned model on 🤗 **Hugging Face** at [MolGen-large](https://huggingface.co/zjunlp/MolGen-large) and [MolGen-large-opt](https://huggingface.co/zjunlp/MolGen-large-opt).**
- **`2023-2` We provide a demo on 🤗 **Hugging Face** at [Space](https://huggingface.co/spaces/zjunlp/MolGen).**

# 📕 Requirements

To run the codes, You can configure dependencies by restoring our environment:
```
conda env create -f MolGen/environment.yml -n $Your_env_name$
```

and then：

```
conda activate $Your_env_name$
```

# 📚 Resource Download

You can download the pre-trained and fine-tuned models via Huggingface: [MolGen-large](https://huggingface.co/zjunlp/MolGen-large) and [MolGen-large-opt](https://huggingface.co/zjunlp/MolGen-large-opt).

Moreover, the dataset used for downstream tasks can be found [here](https://github.com/zjunlp/MolGen/tree/main/moldata/finetune).

The expected structure of files is:

```
moldata
├── checkpoint
│   ├── molgen.pkl # pre-trained model
│ ├── syn_qed_model.pkl # fine-tuned model for QED optimization on synthetic data
│ ├── syn_plogp_model.pkl # fine-tuned model for p-logP optimization on synthetic data
│ ├── np_qed_model.pkl # fine-tuned model for QED optimization on natural product data
│ ├── np_plogp_model.pkl # fine-tuned model for p-logP optimization on natural product data
├── finetune
│   ├── np_test.csv # nature product test data
│   ├── np_train.csv # nature product train data
│   ├── plogp_test.csv # synthetic test data for plogp optimization
│   ├── qed_test.csv # synthetic test data for plogp optimization
│   └── zinc250k.csv # synthetic train data
├── generate # generate molecules
├── output # molecule candidates
└── vocab_list
└── zinc.npy # SELFIES alphabet
```

# 🚀 How to run

+ ## Fine-tune

- First, preprocess the finetuning dataset by generating candidate molecules using our pre-trained model. The preprocessed data will be stored in the folder ``output``.

```shell
cd MolGen
bash preprocess.sh
```

- Then utilize the self-feedback paradigm. The fine-tuned model will be stored in the folder ``checkpoint``.

```shell
bash finetune.sh
```

+ ## Generate

To generate molecules, run this script. Please specify the ``checkpoint_path`` to determine whether to use the pre-trained model or the fine-tuned model.

```shell
cd MolGen
bash generate.sh
```

# 🥽 Experiments

We conduct experiments on well-known benchmarks to confirm MolGen's optimization capabilities, encompassing penalized logP, QED, and molecular docking properties. For detailed experimental settings and analysis, please refer to our [paper](https://arxiv.org/abs/2301.11259).

+ ## MolGen captures real-word molecular distributions

+ ## MolGen mitigates molecular hallucinations
### Targeted molecule discovery

### Constrained molecular optimization

# Citation

If you use or extend our work, please cite the paper as follows:

```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}
}
```

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