What it is
Latent-X is an atom-level generative model for de novo protein binders — macrocycles and mini-binders — offered through a no-code web platform. In lab tests it reported very high hit rates (90%+ for macrocycles) testing only tens of designs. Founded by Simon Kohl, a lead on the original AlphaFold team, Latent Labs licenses models rather than developing its own drugs.
Evidence trail
BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.
Model passport
How Latent-X represents biology
Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.
Biological scale
Modalities & tasks
Registry, claims and frontier intelligence
Version history not yet curated
1 version record · release year not yet normalized. Model-family identity remains separate from capability and access changes.
Explore version lineage →0 normalized claims
No task, dataset, split and metric claim has been normalized for this record yet.
Open claim intelligence →0 connected frontiers
No frontier-research record currently connects to this model.
Inspect research horizon →Inputs and outputs
Inputs
Target structure or design objectiveOutputs
Designed sequencesCandidate structuresScientific and technical profile
Scientific principles
Technology
Scientific lineage
These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.
Atomic structures of biologically important molecules by X-ray crystallography
Dorothy Crowfoot HodgkinStructure-based drug design depends on the experimental structural tradition she helped establish.
Anfinsen’s dogma—the thermodynamic hypothesis
Christian B. AnfinsenProtein structure prediction, inverse folding and generative protein design all assume that sequence strongly constrains structure and function.
Computational protein structure prediction and de novo design
David BakerModern binder design, inverse folding and diffusion-based protein generation build on this computational-design lineage.
The alpha helix, beta sheet and hydrogen-bonded protein structure
Linus Pauling, Robert Corey & Herman BransonProtein representation, fold recognition, structural priors and generative protein design all encode these recurring geometric motifs.
The central dogma and directional information transfer
Francis CrickMulti-omic models and sequence foundation models connect genotype, transcript and protein through this information-flow framework.
Levinthal’s paradox and efficient folding pathways
Cyrus LevinthalModern folding algorithms, energy landscapes, learned priors and diffusion models solve a constrained search problem rather than brute-force conformational enumeration.
Evaluation evidence
BioAtlas has not yet extracted a structured benchmark claim for this record.
Known limitations
- Independent reproducibility is limited by proprietary access.
- A structured benchmark claim has not yet been extracted for this record.
- Outputs require task-specific scientific and experimental validation.
Milestones
Founder co-created AlphaFold at DeepMind.
Reported 91–100% macrocycle hit rates on benchmark targets.