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

Duy Minh Ho Nguyen, Trung Quoc Nguyen, Ha Thi Hong Le, Mai Thanh Nhat Truong, TrungTin Nguyen, Nhat Ho, Khoa D Doan, Duy Duong-Tran, Li Shen, Daniel Sonntag, James Zou, Mathias Niepert, Hyojin Kim, Jonathan E Allen

Keywords: small molecule design, fragment molecule, graph transformer, optimal transport, molecule representation learning

TL;DR: a new method for 2D-3D molecule aggregation

Abstract: 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.

Submission Number: 86