Molecular property prediction plays a crucial role in cheminformatics, yet existing methods are constrained by data scarcity and molecular structural heterogeneity. The Mixture of Experts (MoE) framework adopts a divide-and-conquer approach by partitioning the input space and employing expert models. However, current methods primarily rely on scaffold or atomic-level information, often neglecting fine-grained features such as functional groups. Moreover, existing MoE models lack effective mechanisms to filter redundant and noisy information, limiting prediction accuracy and generalization. To address these challenges, we propose a novel Expert-Guided Substructure Information Bottleneck (ESIB-Mol) framework that integrates MoE learning with the Information Bottleneck (IB) principle to optimize molecular representation learning. ESIB-Mol employs substructure-specific experts to focus on key molecular scaffolds and functional groups, which play a crucial role in determining molecular properties such as bioactivity and pharmacokinetics. Meanwhile, the IB principle is leveraged to filter out redundant and irrelevant information, thereby enhancing prediction accuracy and interpretability. Additionally, a dynamic gating mechanism adaptively assigns molecules to the most relevant expert, optimizing computational efficiency. Extensive experiments on benchmark data sets demonstrate the effectiveness of ESIB-Mol, highlighting its superior performance in molecular property prediction.