AI driven B-cell Immunotherapy Design

TL;DR

This paper reviews recent advances in machine learning tools for B-cell immunotherapy design, highlighting their applications in epitope and antibody prediction, and discusses current challenges and future directions.

Contribution

It provides a comprehensive overview of ML-based methods in B-cell immunotherapy, including data sources, evaluation metrics, and critical analysis of limitations and challenges.

Findings

ML tools improve epitope prediction accuracy

Computational methods accelerate antibody design

Current challenges include data quality and model generalization

Abstract

Antibodies, a prominent class of approved biologics, play a crucial role in detecting foreign antigens. The effectiveness of antigen neutralisation and elimination hinges upon the strength, sensitivity, and specificity of the paratope-epitope interaction, which demands resource-intensive experimental techniques for characterisation. In recent years, artificial intelligence and machine learning methods have made significant strides, revolutionising the prediction of protein structures and their complexes. The past decade has also witnessed the evolution of computational approaches aiming to support immunotherapy design. This review focuses on the progress of machine learning-based tools and their frameworks in the domain of B-cell immunotherapy design, encompassing linear and conformational epitope prediction, paratope prediction, and antibody design. We mapped the most commonly used data sources, evaluation metrics, and method availability and thoroughly assessed their significance and limitations, discussing the main challenges ahead.