A Protein Embedding Is Not an Explanation: Protein Language Models in 2026
A practical guide to choosing, extracting, adapting and validating protein language-model representations without mistaking a system score for biological generalisation.
Updated most weeks/No. 70/Genomics · Proteins · DiscoveryEst. 2024
An applied-AI practice for sequence biology.
Pragmatic advisory and empirical analysis for machine learning in genomics, proteins, and drug discovery — what holds up on a held-out test set, what doesn't, and what it means if you build with it. The writing here is the practice thinking in the open.
Lead articleAug 28, 2026 · 21 min read
A Generated Protein Is Not a Result: Protein Design in 2026
Protein generators propose different biological objects. A defensible design starts with the assay, records the complete computational stack, and preserves every experimental denominator.
#protein-design#generative-biologyRead →
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The beats we cover
All topics →01
Genomics & DNA models
Sequence models, variant-effect prediction, and the work to read non-coding DNA.
23 posts→
02
Proteins & structure
Single-sequence folding, protein language models, and structure without alignment.
6 posts→
03
Drug discovery
Contrastive screening, protein–ligand interaction, and design that survives the wet lab.
5 posts→
04
Interpretability & evaluation
What models encode, where they fail, and how evaluation and oversight hold up.
2 posts→
The practice
The reading is public. The advisory is where it's applied.
rewire.it works with biotech, pharma, and research teams deciding whether and how to adopt a given AI-in-bio method — feasibility reviews, evaluation design, and held-out benchmarking. Independent, candid about where machine learning helps and where it doesn't.