Category: AI &amp; ML

—

by

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

—

by

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

—

by

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

—

by

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

—

by

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?

—

by

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

—

by

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. Recursion’s phenotypic engine (Recursion OS, a phenomics data universe of more than 25 petabytes) has demonstrated a real capability: to surface candidates from cellular phenotypes without knowing the target…

The landscape of AI drug-discovery platforms: the design is proven, but is the clinic?

—

by