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. The 30-second version What. Capital and reproducibility in biology’s foundation models flow in opposite directions: the largest raise sits behind the most closed engine (Isomorphic Labs, a $2.1B Series B in May 2026,…

Benchmark reality for bio foundation models: how data leakage, missing baselines and metric choice inflate “beats baseline” claims

—

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. A leaderboard number for a bio foundation model (bio-FM) is the product of model ability and how forgiving the evaluation is. Data leakage is close to the default, not…

Have cell / virtual-cell foundation models beaten the linear baseline? An honest read (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 is the most skeptical axis of the bio-foundation-model series. As of 2026-07, cell / virtual-cell foundation models (scGPT, Geneformer, scFoundation, Arc STATE and others) do not yet consistently…

Genome foundation models and the “noncoding SOTA” claim: what Evo2 really beats, and where it does not

—

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. Evo2 (Arc Institute · NVIDIA · Stanford · UC Berkeley · UCSF; 7B / 40B parameters, ~9.3 trillion nucleotides of training data) claims a new state of the art…

Protein foundation models (AlphaFold3, ESM3): does beating the specialist baseline mean generalization — or memorization?

—

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. On FoldBench, a leakage-controlled independent benchmark, AlphaFold3 (AF3) leads on most tasks — but its score splits sharply by axis. It is robust on protein monomers (LDDT 0.88) and…

The landscape of biology foundation models: do they actually beat the old specialized baselines?

—

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. Protein, genome and cell “foundation models” (bio-FMs) are usually sold with headlines like “simulating millions of years of evolution.” The firm’s one question is narrower: on a specific task,…

AI protein design’s validation reality — high in-silico scores, low wet-lab hit rates, and why an honest success-rate benchmark is still missing

—

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. 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

—

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, 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

—

by