# ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-Tuned Protein Language Models

## TL;DR

ALLM-Ab is a new method that uses advanced AI to optimize antibodies for better performance and developability in drug design.

## Contribution

Introduces ALLM-Ab, an active learning framework using fine-tuned protein language models for efficient antibody optimization.

## Key findings

- ALLM-Ab outperforms baseline methods in discovering high-affinity antibody variants.
- The framework preserves critical developability metrics during optimization.
- Validated using DMS data and active learning trials across 15 antigens.

## Abstract

Antibody engineering requires a delicate balance between
enhancing
binding affinity and maintaining developability properties. In this
study, we present ALLM-Ab (Active Learning with Language Models for
Antibodies), a novel active learning framework that leverages fine-tuned
protein language models to accelerate antibody sequence optimization.
By employing parameter-efficient fine-tuning via low-rank adaptation,
coupled with a learning-to-rank strategy, ALLM-Ab accurately assesses
mutant fitness while efficiently generating candidate sequences through
direct sampling from the model’s probability distribution.
Furthermore, by integrating a multiobjective optimization scheme incorporating
antibody developability metrics, the framework ensures that optimized
sequences retain therapeutic antibody-like properties alongside improved
binding affinity. We validate ALLM-Ab in both offline experiments
using deep mutational scanning (DMS) data from the BindingGYM data
set and online active learning trials targeting Flex ddG energy minimization
across 15 antigens. Results demonstrate that ALLM-Ab not only expedites
the discovery of high-affinity variants compared to baseline Gaussian
process regression and genetic algorithm-based approaches, but also
preserves critical antibody developability metrics. This work lays
the foundation for more efficient and reliable antibody design strategies,
with the potential to significantly reduce therapeutic development
costs.