Tag: machine learning

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 funding, valuation and milestone totals are attributed to each company’s press releases or media reports; many details cannot be independently verified and are marked as such. Vendor blog/preprint performance…

Bio Foundation Models: Where the Capital Is, Where the Evidence Isn’t, and Why the Two 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 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,…

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

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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. 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?

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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. 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?

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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. 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

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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…

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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