Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
This addresses the problem of designing effective antibodies for therapeutics and biology, though it is incremental as it builds on pre-trained diffusion models.
The paper tackled antigen-specific antibody sequence-structure co-design by optimizing for rational structures and high binding affinities, achieving state-of-the-art performance on the RAbD benchmark with low total energy and high binding affinity.
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.