A generative framework for enhancing drug target interaction prediction in drug discovery

In silico drug-target interaction (DTI) prediction plays a key role in accelerating drug discovery and understanding molecular mechanisms. Traditional methods often struggle with the complexity and scale of biochemical data, thus limiting prediction accuracy. This study presents VGAN-DTI, a generative AI framework that combines generative adversarial networks (GANs), variational autoencoders (VAEs), and multilayer perceptrons (MLPs) to improve DTI predictions. The framework precisely encodes molecular features, uncovers underlying molecular mechanisms, and enhances predictive capabilities. GANs generate diverse molecular candidates, whereas VAEs optimize the feature representations. MLPs trained on BindingDB classify interactions and predict binding affinities. The model achieves 96% accuracy, 95% precision, 94% recall, and 94% F1 score, outperforming existing methods. Rigorous ablation studies validated the robustness of the framework. The proposed system enhances drug discovery by optimizing innovation, synthetic feasibility, and predictive accuracy, ensuring reliable DTI predictions and advancing data-driven pharmaceutical research.