MT_Tox

Information

Official github repository of the MT-Tox

Contact Info:

15pms@gm.gist.ac.kr

hjnam@gist.ac.kr

Environment setting (Anaconda)

You have to download pytorch and dgl wheel files.

Download dgl-1.1.1+cu113-cp37-cp37m-manylinux1_x86_64.whl file from dgl

Download torch-1.12.1+cu113-cp37-cp37m-linux_x86_64.whl file from pytorch

conda create -n MT_Tox python=3.7.16

conda activate MT_Tox

pip install -r requirements.txt

pip install torch-1.12.1+cu113-cp37-cp37m-linux_x86_64.whl

pip install dgl-1.1.1+cu113-cp37-cp37m-manylinux1_x86_64.whl

After setting conda environment, use the following command to use the jupyter notebook

python -m ipykernel --user --name MT_Tox

MT_Tox file explanation

• data : Folder that contains the pre-training & fine-tuning datasets for experiments
• model : Folder that contains the .py files and pre-trained model weights for model training
• Tox21_multitask_training.ipynb : Jupyter notebook files for implementing Tox21 (in vitro toxicological information) trainig
• in_vivo_finetuning.ipynb : Jupyuter notebook files for implementing in vivo toxicity fine tuning
• in_vivo_inference.ipynb : Jupyter notebook files for in vivo toxicity inference for any molecular dataset

LICENSE

The source code in this repository is licensed under the PolyForm Noncommercial License 1.0.0. See the LICENSE file for more information.

The trained model weights and any generated data are licensed under the CC-BY-NC-4.0. See the CC BY-NC-SA 4.0 file for more information.

Third-party Notice (MIT License)

Parts of this code are adapted from BayeshERG and MultiObjectiveOptimization, which are licensed under the MIT License.