Iterative ML and Experiments for Emerging VOCs

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Authors: Thomas Y. Sheffield
Cited by: 0

Abstract

SAND2026-17074O Iterative ML and Experiments for Emerging VOCs is a tool that analyzes and predicts the behaviors of SARS-CoV-2 variants. It processes experimental data on ACE2 (the receptor for the SARS-CoV-2 virus that allows it to infect the cell) and antibody binding using machine learning models, including neural networks, to forecast ACE2 interactions and variant expression. The tool employs transfer learning and global epistasis modeling, integrating public datasets with proprietary data to enhance prediction accuracy. Additionally, it fits concentration-response curves to determine dissociation constants and generates visualizations to support research findings, thereby aiding in the identification of new antibodies for emerging variants of concern. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fields of Study & Subjects

SARS-CoV-2 and COVID-19 Research vaccines and immunoinformatics approaches SARS-CoV-2 detection and testing

Publication details

Journal / source
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)
Publication date
1 January 2026
Language
EN