EVEREST highlights:
✅ Where models fail—and why
✅ Which viruses are least/most predictable
✅ How to estimate per-protein, model-specific reliability
✅ Concrete steps to improve ML for viral mutation prediction
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🌍Current models fail to reliably predict mutations in more than half of the high-priority viruses identified by the WHO.
8/12

💪Is bigger always better? Maybe not for other taxa but for viruses - yes! For viruses, models continue to improve with increased numbers of parameters.
7/12

🤏Why? Viruses are severely underrepresented in training datasets (<1%) and are further downsampled after common clustering approaches.
6/12

📉Despite the hype, protein language models trained across the “protein universe” are outperformed by even the simplest, site-independent alignment-based model.
5/12

🚨New paper 🚨

Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇
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