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Updated most weeks/No. 69/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.

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Lead articleJun 16, 2026 · 25 min read

Fifteen Ways to Fold a Protein: A 2026 Field Guide to Structure Prediction Foundation Models

AlphaFold3's weights are not openly downloadable. Academic researchers must request them through a Google form, granted at DeepMind's sole discretion, with no redistribution and no commercial use. That single sentence reorganizes how you should think about the entire field.

#protein-structure-prediction#foundation-modelsRead →

Latest articles

Filed most weeksAll 69 →
01

Protein Design Foundation Models in 2026: Two Ledgers, One Pipeline, Fourteen Models

RFdiffusion2 scaffolded all 41 of 41 enzyme active sites on a benchmark where the prior best deep-learning method managed 16. That gap is the field in one number: diffusion made backbone generation almost routine on paper, and the slow, honest work is finding out how much survives contact with a pipette. Here are the 14 models, sorted by what they can do, what survives the wet lab, and whether you can legally ship them.

#protein-design#foundation-models#generative-models
25 min read
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The beats we cover

Four standing threadsAll topics →
01

Genomics & DNA models

Sequence models, variant-effect prediction, and the work to read non-coding DNA.

22 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.

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