AntibodyFlow: Normalizing Flow Model for Designing Antibody Complementarity-Determining Regions
This addresses the problem of in silico antibody design for drug discovery, offering a domain-specific incremental improvement over existing methods.
The paper tackles the challenge of designing antibody complementarity-determining regions (CDRs) by learning their 3D geometric structures, proposing AntibodyFlow, a 3D flow model that improves validity rate by up to 16.0% and reduces geometric error by 24.3% compared to baselines.
Therapeutic antibodies have been extensively studied in drug discovery and development in the past decades. Antibodies are specialized protective proteins that bind to antigens in a lock-to-key manner. The binding strength/affinity between an antibody and a specific antigen is heavily determined by the complementarity-determining regions (CDRs) on the antibodies. Existing machine learning methods cast in silico development of CDRs as either sequence or 3D graph (with a single chain) generation tasks and have achieved initial success. However, with CDR loops having specific geometry shapes, learning the 3D geometric structures of CDRs remains a challenge. To address this issue, we propose AntibodyFlow, a 3D flow model to design antibody CDR loops. Specifically, AntibodyFlow first constructs the distance matrix, then predicts amino acids conditioned on the distance matrix. Also, AntibodyFlow conducts constraint learning and constrained generation to ensure valid 3D structures. Experimental results indicate that AntibodyFlow outperforms the best baseline consistently with up to 16.0% relative improvement in validity rate and 24.3% relative reduction in geometric graph level error (root mean square deviation, RMSD).