Advanced Deep Learning Techniques for Drug Target Interaction Prediction in Biomedical Research
Haewon Byeon, Hussein Basim Furaijl, Ahmed Read Al-Tameemi, Hayder Ali Hussein, Tahir Toma Farhan, Mukesh Soni · 2025
In the realm of drug-target interaction prediction, advanced deep learning techniques have revolutionized the field by offering sophisticated methods to predict and analyze interactions with increased accuracy. This study explores the efficacy of several state-of-the-art models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), Autoencoders, Deep Belief Networks (DBNs), Long Short-Term Memory Networks (LSTMs), Transformer-based Models, Transfer Learning Approaches, Hybrid Models (CNN-GNN), and Attention Mechanisms. The proposed method builds on these techniques by introducing iterative enhancements to optimize prediction accuracy and reliability. Key improvements involve refined parameter tuning, dropout regularization, and ensemble learning, coupled with attention mechanisms that focus on critical interaction features. The comprehensive evaluation of these methods reveals that the proposed approach consistently outperforms the existing models across various performance metrics, including accuracy, precision, recall, and computational efficiency. By leveraging these advancements, the proposed method enhances the robustness and efficiency of drug-target interaction predictions, making it a valuable tool for drug discovery and biomedical research. This study underscores the potential of integrating advanced deep learning techniques to address complex challenges in predicting drug-target interactions and advancing the field of computational biology.