  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
sequence-structure co-design as an optimization problem towards specific
preferences, considering both rationality and functionality. Leveraging a
pre-trained conditional diffusion model that jointly models sequences and
structures of 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 simultaneously, demonstrating the
superiority of our approach.
