Tag: science

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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…

Structure prediction, dissected — single-domain static folds are essentially solved (AlphaFold2, AlphaFold3 co-folding, ESMFold, the 2024 Nobel), but disorder, dynamics, PPI, ligand pose and mutation ΔΔG are the real frontiers

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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 accuracy figures (CASP GDT, pLDDT, pTM, PoseBusters) are attributed to the primary paper or its sponsor; peer-reviewed results are separated inline from preprints and company blogs. Benchmark accuracy is…

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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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,…

The Real Bottleneck in AI Drug Discovery Is Biology: Where the Capital Is, Where the Clinic Isn’t, and Why They Point in Opposite Directions

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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. The 30-second version What. Capital and clinical proof in AI drug discovery point in opposite directions. The best-financed pure-AI drug shops — Isomorphic Labs (a $2.1B Series B in May 2026), Xaira ($1B+…

How much of “AI-discovered drugs succeed more” is evidence and how much is narrative: attribution, sample size and the survivorship trap

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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. The 30-second version What. The widely quoted claim that “AI-discovered drugs succeed at roughly twice the rate” traces back, in practice, to a single aggregation by one BCG-adjacent author team (Jayatunga et al.,…

Same primary endpoint, opposite headlines: reading AI-designed drug readouts (rentosertib, EXS-21546, BEN-2293) — Part 3

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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. The 30-second version What. This part dissects individual AI-designed clinical readouts along a single axis: primary endpoint vs. headline. The decisive fact of framing asymmetry: rentosertib (headlined “positive”) and BEN-2293 (headlined “failed”) both…

Structure-based and generative drug design in the clinic: elegant molecules, but whose efficacy?

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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. The 30-second version What. Structure-based and generative design engines (Insilico’s Chemistry42, Isomorphic’s IsoDDE, Schrödinger’s FEP+, Relay’s motion-based platform) have demonstrably done one thing: designed and optimized molecules well enough to enter the clinic,…

Recursion and Exscientia: the phenotypic engine can discover, but the clinic has not confirmed it

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