Minimum Description Length-Driven Fragment Mining for Pretraining Molecule Property Prediction Model

Molecular fragments play a crucial role in molecular property prediction. However, most existing deep learning approaches rely heavily on expert-defined substructural patterns, limiting their ability to identify novel or latent fragments. This constraint reduces the generalizability and applicability of molecular fragments in molecular representation learning. In this study, we propose the Molecular Multi-view Pre-training model with Adaptive Fragment Mining (MMP-AFM), a unified framework that facilitates the seamless integration of molecular structural information. MMP-AFM formulates fragment discovery as a combinatorial optimization problem, using description length as the objective to enable adaptive extraction of molecular fragments and dynamic construction of a fragment library. Additionally, we design a molecular multi-view self-supervised pretraining framework that aligns features from the fragment, global, and data augmentation views, ensuring a comprehensive integration of molecular substructural information. Finally, the MMP-AFM is applied to both molecular classification and regression tasks. Experimental results demonstrate that MMP-AFM consistently outperforms existing methods across multiple tasks, highlighting its broad applicability and efficiency.