AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies
NIAID - National Institute of Allergy and Infectious Diseases
About This Grant
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.
Grant Summary
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies is a NIAID - National Institute of Allergy and Infectious Diseases grant providing up to $486K for university, nonprofit, healthcare org. Applications are due 2027-04-30 (open). Check eligibility and apply with FindGrants.
Not quite the right fit?
Search 9,000+ open grants, or get matches ranked for your organization — free.
Focus Areas
Eligibility
How to Apply
Up to $486K
2027-04-30
- 1Confirm your organization is eligible for AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies from NIAID - National Institute of Allergy and Infectious Diseases, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIAID - National Institute of Allergy and Infectious Diseases before the deadline.
Don't want to draft it yourself?
We'll draft the complete application against NIAID - National Institute of Allergy and Infectious Diseases's requirements, run a quality review, and email you a submission-ready PDF plus an editable Word doc within 5 business days. Most orders deliver in 24-48 hours. Flat $399, any grant size.
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies: Frequently Asked Questions
Who is eligible for the AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies?
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies is offered by NIAID - National Institute of Allergy and Infectious Diseases and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies provide?
AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies provides up to $486K per award from NIAID - National Institute of Allergy and Infectious Diseases. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies deadline?
Applications for AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies are due 2027-04-30 (open). Because deadlines can change, verify the date with the funder, NIAID - National Institute of Allergy and Infectious Diseases, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies?
To apply for AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodies, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIAID - National Institute of Allergy and Infectious Diseases.