AbODE: Ab Initio Antibody Design using Conjoined ODEs
Yogesh Verma Markus Heinonen Vikas Garg
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.