# AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies

> **NIH NIH R01** · MICHIGAN STATE UNIVERSITY · 2024 · $544,602

## Abstract

Project Summary
Due to massive vaccination, coronavirus disease 2019 (COVID-19) pandemic caused by the
severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been partially under control.
However, emerging contagious variants such as Delta are still fueling new waves of infections
around the world. Vaccine-escape (or vaccine-breakthrough) variants pose renewed threats to
our battle against COVID-19. Understanding viral mutagenesis and evolution is of preeminent
importance. By integrating genomic analysis, artificial intelligence (AI), computational biophysics,
advanced mathematics, and experimental data, the PIs have built a comprehensive program with
the experimental level of accuracy and population-level of reliability for predicting SARS-CoV-2
variant infectivity and antibody disruption. It remains challenging to forecast future emerging
vaccine-escape variants, to develop the next-generation of vaccines, and to design mutation-
proof antibody therapeutics. These challenges are tackled in the proposed project. New
mathematical tools and AI algorithms will be developed to further improve the current state-of-
the-art in predicting mutation-induced viral infectivity changes, vaccine breakthroughs, and
antibody disruptions. Vital mutations in future emerging variants will be forecasted based on
molecular mechanisms, natural selection, and evolutionary effects. New mutation-proof antibody
drugs will be designed and tested based on those antibodies that had gone through earlier clinical
trials. The predictive models will be implemented into a user-friendly platform with online servers
for researchers to design mutation-proof new vaccines and antibody therapies. The proposed
methods will be applied to forecast emerging variants in the flu and improve the efficacy of
seasonal flu vaccines.

## Key facts

- **NIH application ID:** 10837801
- **Project number:** 5R01AI164266-03
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Guowei Wei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $544,602
- **Award type:** 5
- **Project period:** 2022-05-06 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10837801

## Citation

> US National Institutes of Health, RePORTER application 10837801, AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies (5R01AI164266-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10837801. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*