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Article

Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.

Journal of chemical information and modeling - 25 May 2026

Durumeric Aleksander E P, McCarty Sean, Smith Jay, Köhler Jonas, Elez Katarina, Raich Lluís, Suriana Patricia A, Sztain Terra

Abstract excerpt

Predicting protein variant effects is a key challenge in preparing for pathogenic viral strains, understanding mutation-linked diseases, and designing new proteins. Protein sequence-structure-function relationships are difficult to model due to complex allosteric and epistatic effects. To investigate efficient modeling strategies, we trained supervised machine learning (ML) models with deep mutational scanning...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 42089465
DOI: 10.1021/acs.jcim.6c00332
PMCID: PMC13213839

Genes

ACE2

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

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