Title: IgFlow-LM: De Novo Antibody Design via Joint Flow Matching on SE(3) and Protein Language Models Probability Flows
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: antibody, artificial intelligent, de novo design, protein language model

Abstract: In this work, we present IgFlow-LM, a multi-modal deep generative model for de novo antibody design based on a flow-matching framework that integrates protein language models (PLMs). By learning the joint distribution over SE(3)-equivariant structural flows and PLM-derived probabilistic flows, IgFlow-LM enables the coordinated generation of antibody 3D structures and latent embeddings in the PLM space. Experimental results demonstrate that, in unconditional design, IgFlow-LM generates antibody structures that closely resemble naturally occurring antibodies. The distributions of backbone dihedral angles and PLM embeddings show strong agreement with those of native antibodies. Moreover, the structures generated by IgFlow-LM exhibit smaller deviations from ideal bond lengths and bond angles compared to reference models. Furthermore, we benchmark IgFlow-LM against baseline models on two common conditional complementarity-determining regions (CDRs) design tasks in antibody engineering. Results show that IgFlow-LM achieves superior self-consistency in CDRs design and generates higher sequence diversity within CDR regions compared to baseline approaches, highlighting its potential for producing structurally accurate and diverse antibody variants.