Quantum informed graph transformer model

Machine learning model that predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of molecules.

Environment setting & installation

mamba env create -f requirements.yml
mamba activate qip
pip install -e .

How to run

Configuration is implemented depending on omegaconf and hydra package. You can refer to the contents of the corresponding package for instructions on how to use it.

Download pretrained weight

https://shorturl.at/bJQWY

create saved_model directory and put weights into the saved_model directory.

train process

You can execute specific configuration through experiment argument.

# bash
bash workflow.sh

python run.py experiment=<your_config_to_run>

# download tdc datset
python preprocessing.py

# multitask learning
python run.py experiment=encoder_train/gps/multitask/gps_MTHAD.yaml seed=8272

# finetuning
python run.py experiment=encoder_train/gps/finetuning/template/ames.yaml seed=8272 
## example 
python run.py experiment=encoder_train/gps/finetuning/template/ames.yaml seed=8272 system.checkpoint_path=${model_dir}/multitask_weight_HAD.ckpt

# If you wanna print full error log, use HYDRA_FULL_ERROR=1 option
HYDRA_FULL_ERROR=1 python run.py experiment=encoder_train/gps/finetuning/template/ames.yaml seed=8272 system.checkpoint_path=${model_dir}/multitask_weight_HAD.ckpt
# inference
python run.py experiment=encoder_train/gps/inference/inference_test.yaml seed=8272

you can change specific argument by passing <arg_name>=

python run.py experiment=<your_config_to_run> datamodule.batch_size=4 callbacks=early_stopping