Vaccinology in the artificial intelligence era
Artificial intelligence (AI) has already transformed vaccine antigen design and could transform the entire vaccinology pipeline, including immune responses and emerging infectious disease prediction, manufacturing and regulatory processes, clinical trial design and implementation, and vaccine access and equity. However, realizing the promise of AI for vaccinology requires more high-quality data.
Authors
- Steve Black
- Philippe Denoël (ORCID: https://orcid.org/0009-0001-9863-6685)
- Lynda M. Stuart (ORCID: https://orcid.org/0000-0001-7606-7883)
- David E. Bloom (ORCID: https://orcid.org/0000-0003-3731-3345)
- Mark B. Feinberg (ORCID: https://orcid.org/0000-0003-1165-4328)
- William R. Schief (ORCID: https://orcid.org/0000-0002-1120-0150)
- Thibault Helleputte
- Magdalena Zwierzyna (ORCID: https://orcid.org/0000-0002-7445-6030)
- Gianmarco Gasperini (ORCID: https://orcid.org/0000-0001-5411-7667)
- Gaël de Lannoy
- Norman W. Baylor
- Rino Rappuoli (ORCID: https://orcid.org/0000-0002-8827-254X)
- Gagandeep Kang (ORCID: https://orcid.org/0000-0002-3656-564X)
- Jakob P. Cramer (ORCID: https://orcid.org/0009-0005-1268-5750)
- Charlie Weller
Institutions
- Scripps Research Institute (US)
- Gates Foundation (US)
- International AIDS Vaccine Initiative (US)
- Harvard University (US)
- University of Washington (US)
- GlaxoSmithKline (Belgium) (BE)
- Institut des Arts de Diffusion (BE)
- Moderna Therapeutics (United States) (US)
- Wellcome Trust (GB)
Publication Details
- Journal
- Science Translational Medicine
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1126/scitranslmed.adu3791
- Citations
- 4
- Primary Topic
- vaccines and immunoinformatics approaches
- Type
- article
- Field-Weighted Citation Impact
- 3.22