Tag: protein design

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Evidence-first notes on bioscience and deep tech, at the edge of the lab and the market. Information only — not investment advice, not medical advice. This is the bottleneck layer of the AI-protein-design series, where its central question resolves. Self-reported in-silico success rates and improvement factors (from original authors and vendors) are separated throughout from…

Function — where AI protein design actually ships binding, catalysis and neutralization, and where it still fails. A ladder: binders on top, enzymes in the middle, antibodies at the bottom

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Evidence-first notes on bioscience and deep tech, at the edge of the lab and the market. Information only — not investment advice, not medical advice. All success-rate, affinity and kcat/KM figures are attributed to the primary paper (Nature/Science) or company IR, with the media tier noted inline. Reported success rates are relative to the candidates…

The generative engine for protein design — backbone diffusion + inverse folding + a self-consistency filter that only checks folding, not function

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Evidence-first notes on bioscience and deep tech, at the edge of the lab and the market. Information only — not investment advice, not medical advice. Every in-silico metric (scRMSD, pLDDT, sequence recovery, designability) is separated from wet-lab validation (X-ray/cryo-EM, binding, activity), and any vendor/lab claim is attributed. Benchmark or preprint SOTA is not read as…

The AI protein-design landscape — structure prediction is near-solved at the headline, but generative de novo design’s real bottleneck is the in-silico-metric-to-wet-lab-function gap

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