An Integrated Graph Neural Network and Reinforcement Learning Framework for Intelligent Drug Discovery

Abstract

The pharmaceutical industry faces substantial challenges in traditional drug discovery, characterized by extensive time requirements and elevated failure rates. This research presents a computational framework synergizing graph neural networks with adaptive reinforcement learning for molecular generation. The methodology employs attention-based message passing for feature extraction, coupled with multi-component reward structures that dynamically adjust during generation. Experimental validation demonstrates superior performance across evaluation metrics, achieving validity rates exceeding 95% while maintaining structural diversity and drug-like properties. The framework introduces hierarchical graph pooling and proximal policy optimization algorithms tailored for chemical space navigation.