Geno-GNN Characterizes Heterogeneous SARS-CoV-2 Fitness Dynamics
Wang et al., published January 12, 2026 in PLoS Computational Biology, introduce Geno-GNN, a graph representation learning model that predicts ACE2 binding affinity and immune escape from SARS-CoV-2 RBD sequences. Applying Geno-GNN to millions of sequences and external deep mutational scanning datasets, they identify two distinct evolutionary fitness trajectories and report that real-world variants predominantly maintain ACE2 affinity while gaining moderate immune evasion.
Scoring Rationale
Peer-reviewed, reproducible model with actionable outputs drives score; novelty limited relative to prior ML genotype–phenotype methods.
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