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

TL;DR

This paper introduces a topology-aware multiscale mixture of experts model for 3D molecular property prediction, capturing diverse spatial interactions more effectively than fixed neighborhood heuristics.

Contribution

It proposes a novel MI-MoE framework with a distance-cutoff expert ensemble and a topological gating encoder using persistent homology, improving 3D molecular graph learning.

Findings

Consistently improves performance across multiple molecular benchmarks.

Effectively captures short-, mid-, and long-range interactions.

Enhances existing 3D molecular backbones with topology-aware routing.

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.