Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies

TL;DR

This paper introduces a three-stage deep learning framework for designing nature-like antibodies with optimized energy alignment, leveraging Pareto optimality to balance multiple design objectives for improved binding affinity.

Contribution

It presents a novel multi-objective optimization approach using Pareto optimality in antibody design, integrating language models and diffusion models for enhanced sequence-structure co-design.

Findings

Achieves a better Pareto front of antibody designs compared to baselines.

Produces antibodies with high binding affinity and natural-like properties.

Demonstrates high stability and efficiency in the design process.

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 using 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 designs. To mitigate conflicting energy preferences, we extend AbDPO (Antibody Direct Preference Optimization) to guide the model toward 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.