{"ID":2839744,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15906","arxiv_id":"2511.15906","title":"Unified all-atom molecule generation with neural fields","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.","short_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 field...","url_abs":"https://arxiv.org/abs/2511.15906","url_pdf":"https://arxiv.org/pdf/2511.15906v1","authors":"[\"Matthieu Kirchmeyer\",\"Pedro O. Pinheiro\",\"Emma Willett\",\"Karolis Martinkus\",\"Joseph Kleinhenz\",\"Emily K. Makowski\",\"Andrew M. Watkins\",\"Vladimir Gligorijevic\",\"Richard Bonneau\",\"Saeed Saremi\"]","published":"2025-11-19T22:18:13Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.BM\"]","methods":"[]","has_code":false,"code_links":[{"ID":606901,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2839744,"paper_url":"https://arxiv.org/abs/2511.15906","paper_title":"Unified all-atom molecule generation with neural fields","repo_url":"https://github.com/prescient-design/funcbind","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}