@inproceedings{
karthikeyan2023conditional,
title={Conditional Generation of Antigen Specific T-cell Receptor Sequences},
author={Dhuvarakesh Karthikeyan and Colin Raffel and Benjamin Vincent and Alex Rubinsteyn},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=SckdgVW3Kq}
}
Training and evaluating large language models (LLMs) for use in the design of antigen specific T-cell receptor (TCR) sequences is challenging due to the complex many-to-many mapping between TCRs and their targets, a struggle exacerbated by a severe lack of ground truth data. Traditional NLP metrics can be artificially poor indicators of model performance since labels are concentrated on a few examples, and functional in-vitro assessment of generated TCRs is time-consuming and costly. Here, we introduce TCR-BART and TCR-T5, adapted from the prominent BART and T5 models, to explore the use of these LLMs for conditional TCR sequence generation given a specific target epitope. To fairly evaluate such models with limited labeled examples, we propose novel evaluation metrics tailored to the sparsely sampled many-to-many nature of TCR-epitope data and investigate the interplay between accuracy and diversity of generated TCR sequences.
Conditional Generation of Antigen Specific T-cell Receptor Sequences
Dhuvarakesh Karthikeyan Colin Raffel Benjamin Vincent Alex Rubinsteyn