MSMPP: Molecular Property Prediction by Integrating Multi-scale Multi-view information with pretrained 3D molecular large model representation.

Abstract

Molecular property prediction is a cornerstone for accelerating drug discovery, providing a computational way to alleviate the low success rate, high cost, and long development cycle of conventional pharmaceutical research and development. However, existing computational methods have obvious limitations: most current deep learning approaches rely on a single molecular view, failing to fully capture the multi-dimensional features of molecular structures; they mainly focus on intra-molecular features while ignoring inter molecular information and cross-task correlations; and limited labeled data severely impairs their generalization to novel molecules. To address these issues, we propose MSMPP, a multi-scale, multi-view fusion framework for molecular property prediction that learns intra- and inter scale features simultaneously. For intra-scale feature learning, MSMPP integrates TxGemma-enhanced 1D sequence representations, Graph Transformer-derived 2D topological graph features, and Uni-Mol-derived 3D molecular conformational features. The Graph Transformer models long-range atomic dependencies, while the two pretrained models provide task-agnostic molecular prior knowledge from large-scale pretraining corpora, thereby improving generalization to novel molecules. For inter-scale feature learning, MSMPP constructs an inter-molecular graph (IMG) that explicitly models global pairwise interactions among chemically similar molecules and also extracts the cross-task features. Evaluations on eight MoleculeNet datasets show that MSMPP significantly outperforms state-of-the-art models, demonstrating its effectiveness in integrating multi-view intra-molecular features, inter-molecular features and cross-task information. Overall, MSMPP provides a competitive tool for molecular property prediction and supports the acceleration of drug discovery workflows.