Unified all-atom molecule generation with neural fields
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.
692069e58c38b39d6a482eb4
Matthieu Kirchmeyer
claimed_verified
2025-11-25T15:53:22.361Z
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Matthieu Kirchmeyer
mkirchmeyer
user
mkirchmeyer
692069e58c38b39d6a482eb5
Pedro O. Pinheiro
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Emma Willett
692069e58c38b39d6a482eb7
Karolis Martinkus
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Joseph Kleinhenz
692069e58c38b39d6a482eb9
Emily K. Makowski
692069e58c38b39d6a482eba
Andrew M. Watkins
692069e58c38b39d6a482ebb
Vladimir Gligorijevic
692069e58c38b39d6a482ebc
Richard Bonneau
692069e58c38b39d6a482ebd
Saeed Saremi
Unified all-atom molecule generation with neural fields
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.
FuncBind, a framework using neural fields and score-based generative models from computer vision, generates diverse atomic structures across modalities, achieving competitive performance in structure-conditioned molecular design.
neural fields score-based generative models structure-conditioned target-conditioned all-atom molecules atomic systems macrocyclic peptides antibody complementarity-determining region loops de novo redesign