Antigen-Specific Antibody Design via Direct Energy-based Preference
  Optimization
Xiangxin Zhou Dongyu Xue Ruizhe Chen Zaixiang Zheng Liang Wang Quanquan Gu
Antibody design, a crucial task with significant implications across various
disciplines such as therapeutics and biology, presents considerable challenges
due to its intricate nature. In this paper, we tackle antigen-specific antibody
design as a protein sequence-structure co-design problem, considering both
rationality and functionality. Leveraging a pre-trained conditional diffusion
model that jointly models sequences and structures of
complementarity-determining regions (CDR) in antibodies with equivariant neural
networks, we propose direct energy-based preference optimization to guide the
generation of antibodies with both rational structures and considerable binding
affinities to given antigens. Our method involves fine-tuning the pre-trained
diffusion model using a residue-level decomposed energy preference.
Additionally, we employ gradient surgery to address conflicts between various
types of energy, such as attraction and repulsion. Experiments on RAbD
benchmark show that our approach effectively optimizes the energy of generated
antibodies and achieves state-of-the-art performance in designing high-quality
antibodies with low total energy and high binding affinity, demonstrating the
superiority of our approach.