Tag: de novo design
-
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
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
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
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, hit-rate and efficiency figures are attributed to the reporting paper or company; some are company blogs or preprints rather than peer-reviewed data (noted inline). In-silico design metrics (pLDDT,…
Uthando Nethemba 