Repository files navigation

📃Paper • 🤗Model • 🔬Space

• 2024-2 We've releasedChatCell, a new paradigm that leverages natural language to make single-cell analysis more accessible and intuitive. Please visit ourhomepage andGithub page for more information.
• 2024-1 Our paperDomain-Agnostic Molecular Generation with Chemical Feedback is accepted by ICLR 2024.
• 2024-1 Our paperMol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models is accepted by ICLR 2024.
• 2023-10 We open-sourceMolGen-7b, which now supports de novo molecule generation!
• 2023-6 We open-sourceKnowLM, a knowledgeable LLM framework with pre-training and instruction fine-tuning code (supports multi-machine multi-GPU setup).
• 2023-6 We releaseMol-Instructions, a large-scale biomolecule instruction dataset for large language models.
• 2023-5 We proposeKnowledge graph-enhanced molecular contrAstive learning with fuNctional prOmpt (KANO) onNature Machine Intelligence, exploiting fundamental domain knowledge in both pre-training and fine-tuning.
• 2023-4 We provide a NLP for science paper-list athttps://github.com/zjunlp/NLP4Science_Papers.
• 2023-3 We release our pre-trained and fine-tuned model on 🤗 Hugging Face atMolGen-large andMolGen-large-opt.
• 2023-2 We provide a demo on 🤗 Hugging Face atSpace.

To run the codes, You can configure dependencies by restoring our environment:

conda env create -f environment.yaml

and then：

conda activate my_env

You can download the pre-trained and fine-tuned models via Huggingface:MolGen-large andMolGen-large-opt.

You can also download the model using the following link:https://drive.google.com/drive/folders/1Eelk_RX1I26qLa9c4SZq6Tv-AAbDXgrW?usp=sharing

Moreover, the dataset used for downstream tasks can be foundhere.

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

• Fine-tune

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

  cd MolGen    bash preprocess.sh

  • Then utilize the self-feedback paradigm. The fine-tuned model will be stored in the foldercheckpoint.

      bash finetune.sh

• Generate

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

  cd MolGenbash generate.sh

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 ourpaper.

• MolGen captures real-word molecular distributions

• MolGen mitigates molecular hallucinations

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

@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}}