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Related Experiment Video

Updated: May 23, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A curriculum learning approach to training antibody language models.

Sarah M Burbach¹, Bryan Briney^1,2,3,4,5

¹Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037 USA.

Biorxiv : the Preprint Server for Biology

|March 10, 2025

Summary

We introduce curriculum learning for antibody language models (AbLMs) to effectively train on mixed unpaired and paired sequence data. Our optimized method, CurrAb, demonstrates superior performance in classification tasks compared to existing models.

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Area of Science:

* Computational biology
* Bioinformatics
* Machine learning in immunology

Background:

* Antibody language models (AbLMs) are increasingly pre-trained using both unpaired and paired antibody sequences.
* Combining these datasets offers potential benefits but faces challenges due to data imbalance and lack of systematic evaluation for data processing and training strategies.
* Existing methods struggle to fully leverage the scale of unpaired data alongside the specificity of paired data.

Purpose of the Study:

* To develop and evaluate a systematic method for pre-training AbLMs using a mixture of unpaired and paired antibody sequences.
* To address the data imbalance challenge inherent in combining large-scale unpaired datasets with smaller, high-quality paired datasets.
* To optimize training strategies that maximize the utility of both data types for improved AbLM performance.

Main Methods:

* Introduction of a curriculum learning approach for AbLMs, enabling a phased training process from unpaired to paired sequences.
* Optimization of data processing and training methodologies tailored for mixed-data AbLM pre-training.
* Development and evaluation of a 650M-parameter model named CurrAb.

Main Results:

* The developed curriculum learning method effectively integrates unpaired and paired antibody sequences during AbLM pre-training.
* The CurrAb model, utilizing this method, significantly outperforms existing mixed AbLMs on downstream classification tasks.
* Optimized training strategies successfully mitigate the challenges posed by data imbalance.

Conclusions:

* Curriculum learning presents a viable and effective strategy for pre-training AbLMs on mixed sequence datasets.
* The CurrAb model establishes a new benchmark for performance in antibody sequence-based classification tasks.
* This work provides a foundational framework for future development of AbLMs leveraging diverse antibody sequence data.