TeroACT: Constructing terpenoid-bioactivity knowledge graph for drug discovery

• Terpenoid-disease association (TerDA) prediction model as an effective prioritization tool for screening potential disease associations, and facilitating follow-up studies such as drug repositioning and the prediction of new indications for terpenoids.

• This repository contains the source code ,the data and trained models.

Train

Model training can be started by running the run.py script:

python run.py --gpu 0 --epoch 300 --neg_ratio 1 

• --neg_ratio is required here to specify the ratio between negative and positive data

Notes:

You can utilize ./utils/condition.py to generate the required molecular descriptor files in advance, such as: ./dataset/description.csv

Predict

• You can run the prediction.py script to reproduce the predicted results of compounds in web experiment as described in the article. Such as :

python prediction.py

Notes:

For the disease prediction, you can access our user-friendly web server ( TeroACT).