Advancing Bayesian Optimization via Learning Correlated Latent Space (CoBO)

Official PyTorch Implementation for Advancing Bayesian Optimization via Learning Correlated Latent Space (CoBO) (arxiv).

Seunghun Lee*, Jaewon Chu*, Sihyeon Kim*, Juyeon Ko, Hyunwoo J. Kim, In Advanced in Neural Information Processing Systems (NeurIPS 2023).

Setup

We provide setup script file and environment file.

To setup the project, you can use the provided YAML file by running the following command:

conda env create -f requirements.yml

Or, for a shell script setup, run:

sh setup.sh

Run

This repository uses tasks from the GuacaMol benchmark. Run a task with:

python3 scripts/molecule_optimization.py --task_id [TASK] run_cobo done

Available [TASK] codes include:

• med1: Median molecules 1
• pdop: Perindopril MPO
• adip: Amlodipine MPO
• rano: Ranolazine MPO
• osmb: Osimertinib MPO
• zale: Zaleplon MPO
• valt: Valsartan SMARTS
• med2: Median molecules 2
• siga: Sitagliptin MPO
• dhop: Deco Hop
• shop: Scaffold Hop
• fexo: Fexofenadine MPO

For more tasks, see the GuacaMol benchmark page.

Citation

@inproceedings{lee2023advancing,
  title={Advancing Bayesian Optimization via Learning Correlated Latent Space},
  author={Lee, Seunghun and Chu, Jaewon and Kim, Sihyeon and Ko, Juyeon and Kim, Hyunwoo J},
  booktitle={Advances in Neural Information Processing Systems},
  year={2023}
}

Acknowledgements

This repository is based on lolbo.

License

Code is released under MIT License.