From Fragments to Geometry: A Unified Graph Transformer for Molecular Representation from Conformer Ensembles

Designing and understanding molecules for biological applications requires models that can integrate rich structural information from both 2D molecular graphs and diverse 3D conformer ensembles. We introduce a fragment-aware, structure-guided graph transformer that enables scalable and expressive molecular modeling by aggregating multiple 3D conformers while incorporating fragment-level inductive biases from the 2D topology. Our approach employs a trainable attention-based fusion mechanism within a graph transformer to dynamically combine 2D and 3D representations, moving beyond static solvers and rigid fusion heuristics. This architecture enables fine-grained reasoning over chemically diverse molecules, including organocatalysts and transition-metal complexes. While originally developed for molecular property prediction, the method’s structure-aware and fragment-level modeling is readily applicable to generative molecular design, enabling downstream applications in drug discovery, reaction modeling, and AI-driven biological research. The model scales to large datasets and achieves state-of-the-art results across molecular property benchmarks, demonstrating its potential as a foundational component for generative AI in molecular science.