ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacy

Unanticipated drug-drug interactions (DDIs) present a substantial risk of severe bodily harm, underscoring the critical need for predicting adverse effects in polypharmacy. This paper introduces ADEP, a novel approach that integrates a discriminator and an encoder-decoder model to address data sparsity and enhance feature extraction accuracy.

Unzip the event.zip file to access the data.

$ python3 main.py

Requirement

• torch
• numpy
• pandas
• scikit-learn
• matplotlib
• torchmetrics
• git+https://github.com/m0hssn/Metrica.git

Use the following command to install all dependencies.

    pip install requirement.txt    

Citation

Please kindly cite the paper if you use the code or the datasets in this repo:

Katayoun Kobraei, Mehrdad Baradaran, Seyed Mohsen Sadeghi, Raziyeh Masumshah and Changiz Eslahchi, ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacy, 2024, 10.48550/arXiv.2406.00118