Title: FACET: A Fragment-Aware Conformer Ensemble Transformer
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: molecular properties prediction, 3D conformers, graph transformer, 2D-3D fusion, fragment aware module, Fused Gromov-Wasserstein distance

Abstract: Accurately predicting molecular properties requires effective integration of structural information from both 2D molecular graphs and their corresponding equilibrium conformer ensembles. In this work, we propose a scalable Structure-Aware Graph Transformer that efficiently aggregates features from multiple 3D conformers while incorporating fragment-level information from 2D graphs. Unlike prior methods that depend on static geometric solvers or rigid fusion strategies, our approach employs a trainable attention-based mechanism within a graph transformer to dynamically fuse 2D and 3D representations. We further enhance this mechanism by injecting fragment-specific structural biases into the attention layers, enabling the model to capture fine-grained molecular details. Our method scales to large datasets, handling up to 75,000 molecules and hundreds of thousands of conformers, and achieves state-of-the-art results in molecular property prediction and reaction-level modeling. It is particularly effective on chemically diverse compounds, including organocatalysts and transition-metal complexes.