Graph Neural Network-Based Framework for Drug Discovery and Molecular Interaction Prediction

Abstract

The process of drug discovery is complex, time-consuming, and resource-intensive, requiring advanced computational techniques to accelerate molecular analysis and prediction. This paper presents a Graph Neural Network-Based Framework for Drug Discovery and Molecular Interaction Prediction. The proposed system models molecular structures as graphs, where atoms are represented as nodes and chemical bonds as edges, enabling effective learning of structural relationships. Graph Neural Networks are employed to capture spatial dependencies and predict drug-target interactions with high accuracy. The framework integrates multi-modal biomedical data, including chemical properties, genomic information, and protein structures, to enhance predictive performance. Additionally, attention mechanisms are incorporated to identify critical substructures contributing to molecular activity. The system supports both classification and regression tasks for drug efficacy and toxicity prediction. Experimental results demonstrate that the proposed approach outperforms traditional machine learning models in terms of accuracy and generalization. Integration with prior research in healthcare analytics further enhances system robustness. The study highlights the potential of graph-based deep learning techniques in accelerating drug discovery and improving precision medicine.