AtomBloom: All-Atom Branching Flows for 3D Binder Design

Designing target-specific small molecules, peptides, and antibodies requires generating atomistic 3D structures whose cardinality, atom types, and geometry are jointly adapted to the target protein environment. Existing atom-level diffusion and flow models typically fix atom count throughout generation, while hierarchical latent models determine residue or fragment types before all-atom decoding. We introduce AtomBloom, a pocket-specific generative framework that directly generates ligand atoms and allows them to split or disappear along the generative trajectory. This formulation jointly generates atom counts, types, and 3D geometry within a unified framework for small molecules, peptides, and antibodies. For practical generation, AtomBloom combines exact interval event probabilities with dynamic rescaling that adapts the predictor's coordinate scale along the trajectory. We further introduce model-rolled self-conditioning during training and propagate previous predictions along branching lineages during inference. Across small-molecule, peptide, and antibody benchmarks, AtomBloom achieves the best overall ranking on small molecules. For peptides and antibodies, AtomBloom-R adaptively generates each residue’s atoms at fixed residue count and improves sequence recovery, structural accuracy, and binding scores. Further adapting residue count yields additional binding gains, with favorable peptide designs occurring at both longer and shorter lengths than the reference. These results support treating binder cardinality as a design variable alongside atom types and geometry.