An Enhanced Deep Learning Predictive Framework for Drug Target Indications in Alzheimer's Disease
Sheo Kumar, Amritpal Singh · Current Bioinformatics · 2025
Introduction: Alzheimer's disease (AD) is still one of the most challenging neurodegenerative disorders with no available therapeutics. The identification of drug targets is important for effective treatment development. This research introduces an innovative and improved deep learning prediction framework, BiLSTM-AD, aimed toward therapeutic target indication in crucial, complicated illnesses, particularly Alzheimer's disease. Methods: This paper proposes a deep learning framework, BiLSTM-AD, to predict drug-target indications for Alzheimer's Disease using Protein-Protein Interaction (PPI) datasets from String-DB and InTAct databases. The model utilizes Dual Mode Self-Attention to better capture local and global dependencies in the PPI data. This attention mechanism enables the model to learn from the most significant interactions, which has resulted in better prediction accuracy. Results: The BiLSTM-AD model is evaluated with Precision, Recall, and F1-Score, among other metrics, and compared to reactions-based baseline models such as ANN, RNN, CNN, LSTM, and GNN of the same architecture. Testing on PPI datasets, compared to those baseline methods, BiLSTM-AD has achieved 96% and 97.3% prediction accuracy and provides a more robust solution to infer AD-associated targets for potential drugs. Discussion: The model's self-attention mechanism integrates max and average pooling to capture and emphasize both dominating local interactions and global context. Using a dual perspective greatly enhances the model's ability to discover possible therapeutic targets in important proteinprotein interactions. Precision, Recall, F1-Score, AUC, AUPR, and MSE show that the BiLSTMAD model outperforms other benchmark models in prediction accuracy. With 96% accuracy and 0.93 AUC, our technique surpasses strong baselines (CNN, LSTM, GNN). Conclusion: Integrating the BiLSTM and dual mode self-attention mechanism is an especially effective approach to improve the model performance and enables a practical focus on interaction among drug candidates.