Official PyTorch implementation of the "Inversion-based Latent Bayesian Optimization". (NeurIPS 2024)
Jaewon Chu*, Jinyoung Park*, Seunghun Lee, Hyunwoo J. Kim†.
- Clone repository
git clone https://github.com/mlvlab/InvBO.git
cd InvBO
- Install Environment
conda env create -f invbo.yml
conda activate invbo
pip install molsets==0.3.1 --no-deps
This repository provides InvBO applied to CoBO [Lee et al., NeurIPS 2023] for small budget setting.
python exec.py --cuda 0 --task_id [TASK]
Since we predefined the coefficients for VAE loss terms in exec.py provided by CoBO, the available tasks for [TASK] are:
| task_id | Task Name |
|---|---|
| med2 | Median molecules 2 |
| pdop | Perindopril MPO |
| osmb | Osimertinib MPO |
| adip | Amlodipine MPO |
| zale | Zaleplon MPO |
| valt | Valsartan SMARTS |
| rano | Ranolazine MPO |
However, we can also run on the remaining Guacamol tasks when we define coefficients for VAE loss terms in exec.py:
| task_id | Task Name |
|---|---|
| med1 | Median molecules 1 |
| siga | Sitagliptin MPO |
| dhop | Deco Hop |
| shop | Scaffold Hop |
| fexo | Fexofenadine MPO |
You can track the optimization process using the wandb library.
You can use wandb tracking by simply setting '--track_with_wandb', 'True' and '--wandb_entity', 'YOUR ENTITRY' in exec.py.
This repository is based on CoBO.