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

TL;DR

ADEP introduces a discriminator-enhanced encoder-decoder model that significantly improves the accuracy of predicting adverse drug effects in polypharmacy, addressing data sparsity and enhancing feature extraction.

Contribution

The paper presents a novel discriminator-augmented encoder-decoder architecture for adverse effect prediction, outperforming existing methods on benchmark datasets.

Findings

ADEP achieves higher accuracy and AUROC than existing models.

The model effectively handles data sparsity in medical datasets.

Real-world case study confirms practical utility of ADEP.

Abstract

Motivation: Unanticipated drug-drug interactions (DDIs) pose significant risks in polypharmacy, emphasizing the need for predictive methods. Recent advancements in computational techniques aim to address this challenge. Methods: We introduce ADEP, a novel approach integrating a discriminator and an encoder-decoder model to address data sparsity and enhance feature extraction. ADEP employs a three-part model, including multiple classification methods, to predict adverse effects in polypharmacy. Results: Evaluation on benchmark datasets shows ADEP outperforms well-known methods such as GGI-DDI, SSF-DDI, LSFC, DPSP, GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, Random Forest, K-Nearest-Neighbor, Logistic Regression, and Decision Tree. Key metrics include Accuracy, AUROC, AUPRC, F-score, Recall, Precision, False Negatives, and False Positives. ADEP achieves more accurate predictions of adverse effects in polypharmacy. A case study with real-world data illustrates ADEP's practical application in identifying potential DDIs and preventing adverse effects. Conclusions: ADEP significantly advances the prediction of polypharmacy adverse effects, offering improved accuracy and reliability. Its innovative architecture enhances feature extraction from sparse medical data, improving medication safety and patient outcomes. Availability: Source code and datasets are available at https://github.com/m0hssn/ADEP.