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categories: ["genetics", "foundations"]categories: ["genetics", "foundations"]tags: ["machine learning", "protein design", "structural biology", "alphafold", "biosecurity", "drug discovery"]tags: ["machine learning", "protein design", "structural biology", "alphafold", "biosecurity", "drug discovery"]summary: "Machine-learning systems that predict what shape a protein sequence folds into and that generate new proteins with no natural counterpart."summary: "Machine-learning systems that predict what shape a protein sequence folds into and that generate new proteins with no natural counterpart."updated: "2026-10-27"updated: "2026-10-29"humanEvidence: "A computationally designed nanoparticle scaffold is a component of a COVID-19 vaccine approved for use in people in South Korea, and a designed cytokine mimic reached early-phase human trials; no de novo designed protein is approved as a therapeutic."humanEvidence: "A computationally designed nanoparticle scaffold is a component of a COVID-19 vaccine approved for use in people in South Korea, and a designed cytokine mimic reached early-phase human trials; no de novo designed protein is approved as a therapeutic."access: "Most prediction and design code is open source and the AlphaFold structure database is public, so the models are free to run; the gene synthesis and wet-lab testing that turn a design into a molecule are not."access: "Most prediction and design code is open source and the AlphaFold structure database is public, so the models are free to run; the gene synthesis and wet-lab testing that turn a design into a molecule are not."reversibility: "context"reversibility: "context"lines 155–161 → 155–161140 unchanged lines not shown
Three failure modes are well documented. Intrinsically disordered regions, a large fraction of theThree failure modes are well documented. Intrinsically disordered regions, a large fraction of thehuman proteome, have no single structure to predict; the models flag them with low confidence, whichhuman proteome, have no single structure to predict; the models flag them with low confidence, whichis useful, but a confidence score is not a description. Prediction of complexes, includingis useful, but a confidence score is not a description. Prediction of complexes, includingantibody–antigen pairs, remains substantially worse than prediction of single chains, which is oneantibody-antigen pairs, remains substantially worse than prediction of single chains, which is onereason CASP has kept running. Most importantly for medicine, the models are largely insensitive to singlereason CASP has kept running. Most importantly for medicine, the models are largely insensitive to singleamino acid substitutions: a sequence carrying a destabilising disease mutation usually returnsamino acid substitutions: a sequence carrying a destabilising disease mutation usually returnsalmost the same confident structure as the healthy one.[^buel2022] Benchmark studies of drug dockingalmost the same confident structure as the healthy one.[^buel2022] Benchmark studies of drug dockinglines 204–219 → 204–22042 unchanged lines not shown
function rather than form, since a de novo enzyme matching a natural one would be a stronger resultfunction rather than form, since a de novo enzyme matching a natural one would be a stronger resultthan any binder.than any binder. The deflationary case deserves stating. Producing candidate molecules was never the slowest step inThe deflationary case starts further down the pipeline. Producing candidate molecules was never thedeveloping a drug; recruitment and clinical failure are, and the largest single cause of thatslowest step in developing a drug; recruitment and clinical failure are, and the largest singlefailure is lack of efficacy rather than any property of the molecule, which better structures docause of that failure is lack of efficacy rather than any property of the molecule, which betternot address. The same argument shapes debate over [[ai-drug-discovery]] and over whether machine-designedstructures do not address. The same argument shapes debate over [[ai-drug-discovery]] and over[[targeted-drug-delivery|delivery vehicles]] and [[lipid-nanoparticles|nanoparticle formulations]]whether machine-designed [[targeted-drug-delivery|delivery vehicles]] andshorten timelines in practice. Demis Hassabis has presented AlphaFold as an early demonstration of[[lipid-nanoparticles|nanoparticle formulations]] shorten timelines in practice. Demis Hassabis haswhat progress toward [[artificial-general-intelligence]] would buy for science. The counter-argumentpresented AlphaFold as an early demonstration of what progress towardis not that the achievement is small but that the problem was unusually well posed: a discrete input,[[artificial-general-intelligence]] would buy for science. The counter-argument grants thea checkable output, and fifty years of curated experimental structures to learn from. Most ofachievement and holds that the problem was unusually well posed: a discrete input, a checkablebiology offers none of those.output, and fifty years of curated experimental structures to learn from. Most of biology offersnone of those. ## See also## See also removed, struck through added, underlinedLine numbers count the serialised markdown of each revision, frontmatter included.
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