Topology-Aware Multiscale Mixture of Experts for Efficient Molecular Property Prediction

• URL: http://arxiv.org/abs/2601.12637v1
• Date: Mon, 19 Jan 2026 00:54:24 GMT
• Title: Topology-Aware Multiscale Mixture of Experts for Efficient Molecular Property Prediction
• Authors: Long D. Nguyen, Kelin Xia, Binh P. Nguyen,
• Abstract summary: We propose Multiscale Interaction Mixture of Experts (MI-MoE) to adapt interaction modeling across geometric distances.<n>Our contributions are threefold: (1) we introduce a distance-cutoff expert ensemble that explicitly captures short-, mid-, and long-range interactions without committing to a single cutoff; (2) we design a topological gating encoder that routes inputs to experts using filtration-based descriptors, including persistent homology features; and (3) we show that MI-MoE is a plug-in module that consistently improves multiple strong 3D molecular backbones across diverse molecular and polymer property prediction benchmark datasets.
• Score: 4.631359750094176
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many molecular properties depend on 3D geometry, where non-covalent interactions, stereochemical effects, and medium- to long-range forces are determined by spatial distances and angles that cannot be uniquely captured by a 2D bond graph. Yet most 3D molecular graph neural networks still rely on globally fixed neighborhood heuristics, typically defined by distance cutoffs and maximum neighbor limits, to define local message-passing neighborhoods, leading to rigid, data-agnostic interaction budgets. We propose Multiscale Interaction Mixture of Experts (MI-MoE) to adapt interaction modeling across geometric regimes. Our contributions are threefold: (1) we introduce a distance-cutoff expert ensemble that explicitly captures short-, mid-, and long-range interactions without committing to a single cutoff; (2) we design a topological gating encoder that routes inputs to experts using filtration-based descriptors, including persistent homology features, summarizing how connectivity evolves across radii; and (3) we show that MI-MoE is a plug-in module that consistently improves multiple strong 3D molecular backbones across diverse molecular and polymer property prediction benchmark datasets, covering both regression and classification tasks. These results highlight topology-aware multiscale routing as an effective principle for 3D molecular graph learning.

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