Graph Neural Networks for Drug Discovery

Abstract

Drug discovery is a complex and time-consuming process that involves analyzing vast chemical and biological data to identify potential therapeutic compounds. This paper presents a framework based on Graph Neural Networks for Drug Discovery, leveraging graph-based representations of molecular structures to capture intricate relationships between atoms and bonds. The proposed system utilizes Graph Convolutional Networks and related architectures to learn molecular embeddings and predict properties such as drug-target interactions, toxicity, and efficacy. By modeling molecules as graphs, the framework effectively captures structural dependencies that traditional methods often overlook. The approach is further enhanced through integration with deep learning and hybrid optimization techniques to improve prediction accuracy and scalability. Experimental results demonstrate that the proposed model outperforms conventional machine learning approaches in identifying promising drug candidates. Additionally, integration with prior research in healthcare analytics strengthens the robustness and generalization of the system. The study highlights the potential of graph-based deep learning in accelerating drug discovery and reducing development costs in pharmaceutical research.