AbODE: Ab initio antibody design using conjoined ODEs

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Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens holds the key to accelerating vaccine discovery. However, this co-design of the amino acid sequence and the 3D structure subsumes and accentuates, some central challenges from multiple tasks including protein folding (sequence to structure), inverse folding (structure to sequence), and docking (binding). We strive to surmount these challenges with a new generative model AbODE that extends graph PDEs to accommodate both contextual information and external interactions. Unlike existing approaches, AbODE uses a single round of full-shot decoding, and elicits continuous differential attention that encapsulates, and evolves with, latent interactions within the antibody as well as those involving the antigen. We unravel fundamental connections between AbODE and temporal networks as well as graph-matching networks. The proposed model significantly outperforms existing methods on standard metrics across benchmarks.
Original languageEnglish
Title of host publicationProceedings of the 40th International Conference on Machine Learning
EditorsAndread Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett
Number of pages14
Publication statusPublished - Jul 2023
MoE publication typeA4 Conference publication
EventInternational Conference on Machine Learning - Honolulu, United States
Duration: 23 Jul 202329 Jul 2023
Conference number: 40

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
Country/TerritoryUnited States


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