ArticleNature communications2023
Predicting the antigenic evolution of SARS-COV-2 with deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- A deep mutational scanning-informed protein language model predicts SARS-CoV-2 evolution dynamics with spatiotemporal resolution.Nature microbiology · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning.PLoS computational biology · 2026Article
- Influenza vaccine strain selection with an AI-based evolutionary and antigenicity model.Nature medicine · 2025Article
- The mutational landscape of SARS-CoV-2 provides new insight into viral evolution and fitness.Nature communications · 2025Article
- A protein language model for exploring viral fitness landscapes.Nature communications · 2025Article
- Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution.Briefings in bioinformatics · 2025Article
- Design and application of expression constructs for FMDV serotype O structural proteins.Biotechnology letters · 2025Article
- Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.Nature biomedical engineering · 2025Article
- Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time.Nucleic acids research · 2025Article
- Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data.Nature communications · 2025Article
- Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model.Frontiers in public health · 2025Article
- Predicting pathogen evolution and immune evasion in the age of artificial intelligence.Computational and structural biotechnology journal · 2025Review
- Article
- Evolutionary trajectory estimation via replication simulation of coronavirus spike gene based on random mutation and similarity-based selection.Computational and structural biotechnology journal · 2025Article
- Integrating genomic epidemiology and deep mutational scanning data for prevalence forecasting of SARS-CoV-2 Omicron lineages.PloS one · 2025Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- A predictive language model for SARS-CoV-2 evolution.Signal transduction and targeted therapy · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.