Reinforcement Learning Strategies for Multi-Scale Geometric Representation in Molecular Property Prediction

Abstract

Molecular property prediction represents a cornerstone of computational drug discovery, where accurate prediction of molecular properties from structural representations enables accelerated pharmaceutical development and reduced experimental costs. Traditional approaches rely on hand-crafted molecular descriptors or simple graph representations that fail to capture the rich multi-scale geometric information inherent in molecular structures. This paper presents a novel framework that integrates Reinforcement Learning (RL) strategies with multi-scale geometric representations for enhanced molecular property prediction. The proposed Multi-Scale Geometric Reinforcement Learning (MSGRL) framework combines graph neural networks operating at different geometric scales with adaptive reinforcement learning agents that learn optimal feature extraction strategies. Our approach employs a hierarchical representation scheme that captures molecular information from irregular geometric manifolds to structured numerical encodings, while reinforcement learning agents dynamically adjust the importance weights of different representation modalities based on prediction performance feedback. The framework addresses key challenges including geometric data irregularity, multi-modal representation integration, and adaptive learning across diverse molecular property types. Experimental evaluation across diverse molecular property prediction tasks demonstrates significant improvements over state-of-the-art approaches, with performance gains comparable to the best D-MPNN Features across benchmark datasets including QM9, ESOL, FreeSolv, Tox21, and BBBP. The adaptive nature of the reinforcement learning component enables the framework to automatically discover optimal geometric representation strategies for different molecular property types, eliminating the need for manual feature engineering while providing interpretable insights into structure-property relationships.