AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
Keywords: Antibody Design, Diffusion Models, Multi-Objective Optimization
Abstract: We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design.
We first pre-train a language model based on millions of antibody sequence data.
Then, we employ the learned representations to guide the training of a diffusion model for joint optimization over both sequence and structure of antibodies.
During the final alignment stage, we optimize the model to favor antibodies with low repulsion and high attraction to the antigen binding site, enhancing the rationality and functionality of the design.
To mitigate conflicting energy preferences, we extend AbDPO (Antibody Direct Preference Optimization) to guide the model towards Pareto optimality under multiple energy-based alignment objectives.
Furthermore, we adopt an iterative learning paradigm with temperature scaling, enabling the model to benefit from diverse online datasets without requiring additional data.
In practice, our proposed methods achieve high stability and efficiency in producing a better Pareto front of antibody designs compared to top samples generated by baselines and previous alignment techniques.
Through extensive experiments, we showcase the superior performance of our methods in generating nature-like antibodies with high binding affinity consistently.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 8636
Loading