Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines – an Emerging Trend
Felipe Leno da Silva, Mikel Landajuela, Edwin A. Saada, Piyush Karande, Sudeep Sarma, Igor D, Simone Conti, Daniel Faissol
Abstract
The development of novel effective medical treatments is one of the most important and expected beneficial effects of the AI revolution. This decade is witnessing the rise of AI models able to predict complex properties for protein-protein interactions that hold great promise in assisting in the development of antibody therapeutics and vaccines, including for diseases that long eluded us in the pursuit of an effective treatment. This paper introduces this area of research in a language accessible to an AI researcher, exploring the biological problems that can be solved by AI models, as well as the general context to make solutions feasible in practical scenarios. We survey the main current trends and works in this research area and point towards current still unsolved challenges and trade offs. We expect this paper will be extremely helpful for AI researchers trying to join the field, as well as for researchers already working in one of the subtopics that wish to have a better understanding of the general context around it.
BibTeX
@inproceedings{aaai2026_machinelearningm,
title = {Machine Learning Models Assisting the Development of Antibody Therapeutics and Vaccines – an Emerging Trend},
author = {Felipe Leno da Silva and Mikel Landajuela and Edwin A. Saada and Piyush Karande and Sudeep Sarma and Igor D and Simone Conti and Daniel Faissol},
booktitle = {AAAI 2026},
year = {2026}
}