Unified all-atom molecule generation with neural fields
Authors: name: Matthieu Kirchmeyer; name: Pedro O. Pinheiro; name: Emma Willett; name: Karolis Martinkus; name: Joseph Kleinhenz; name: Emily K. Makowski; name: Andrew M. Watkins; name: Vladimir Gligorijevic; name: Richard Bonneau; name: Saeed Saremi
Published: 2025-11-19 22:18:13

Abstract: Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBind uses neural fields to represent molecules as continuous atomic densities and employs score-based generative models with modern architectures adapted from the computer vision literature. This modality-agnostic representation allows a single unified model to be trained on diverse atomic systems, from small to large molecules, and handle variable atom/residue counts, including non-canonical amino acids. FuncBind achieves competitive in silico performance in generating small molecules, macrocyclic peptides, and antibody complementarity-determining region loops, conditioned on target structures. FuncBind also generated in vitro novel antibody binders via de novo redesign of the complementarity-determining region H3 loop of two chosen co-crystal structures. As a final contribution, we introduce a new dataset and benchmark for structure-conditioned macrocyclic peptide generation. The code is available at https://github.com/prescient-design/funcbind.