What it is
ProteinMPNN solves the inverse-folding problem: given a target backbone, it designs amino-acid sequences that reliably fold to it, dramatically outperforming physics-based design. LigandMPNN adds awareness of small molecules, metals and nucleic acids for functional-site design.
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 ProteinMPNN / LigandMPNN 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
LigandMPNN
2 version records · latest curated year 2023. 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 →1 connected frontier
De novo binders · Prospective demonstration
Inspect research horizon →Connected research frontiers
These records describe active research directions, not guaranteed capabilities of this model. Evidence stages and unresolved questions are preserved separately.
Prospective de novo binder generation
Google DeepMind · 2024-09-05Can AI generate high-affinity protein binders for diverse targets with fewer rounds of experimental optimization?
Evidence boundary and unresolved questions
Performance varies by target; the system did not succeed on every attempted target and is not publicly released for unrestricted reproduction.
- Which target properties predict designability?
- How transferable are success rates to membrane and flexible targets?
- Can developability, immunogenicity and function be optimized jointly?
protein binders · prospective validation · generative design · wet labOpen frontier record →Inputs and outputs
Inputs
Protein backboneOptional non-protein atomic contextOutputs
Protein sequencesScientific 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.
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.
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.
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
- Performance depends on the evaluation dataset and operating conditions.
- A structured benchmark claim has not yet been extracted for this record.
- Outputs require task-specific scientific and experimental validation.
Milestones
The standard partner to RFdiffusion.
Runs in seconds on a laptop GPU.