What it is
Iambic builds deep-learning models that fuse quantum-chemical priors with data. NeuralPLexer predicts protein–ligand complex structures and induced-fit conformational changes; Enchant is a multi-task foundation model for potency and ADMET properties trained across programs to predict clinical-stage behavior early.
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 NeuralPLexer / Enchant 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 →2 connected frontiers
Cryptic pockets · Developer-reported
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.
Finding cryptic pockets from sequence
Isomorphic Labs · 2026-02-10Can a model reveal ligandable pockets that are hidden in the unbound protein and only open after a ligand or allosteric change?
Evidence boundary and unresolved questions
The public evidence is currently a company technical report and benchmark narrative. Prospective medicinal-chemistry validation and independent replication remain essential.
- How often are predicted pockets experimentally ligandable rather than geometrically plausible?
- How robust is pocket discovery across membrane proteins, intrinsically disordered regions and low-data target families?
- Can calibrated confidence distinguish genuine induced pockets from model hallucinations?
cryptic pockets · allostery · induced fit · ligandability · dockingOpen frontier record →Induced-fit co-folding beyond familiar targets
Isomorphic Labs · 2026-02-10Can structure models represent large ligand-driven protein rearrangements when the target, pocket or conformational transition is far from training examples?
Evidence boundary and unresolved questions
Out-of-distribution claims depend strongly on benchmark construction, training-set leakage controls and exact success thresholds.
- How are unseen chemotypes and target families isolated from training data?
- Does structural accuracy translate into enrichment or medicinal-chemistry decisions?
- How stable are alternative conformational ensembles?
co-folding · induced fit · OOD generalization · protein flexibilityOpen frontier record →Inputs and outputs
Inputs
Molecular structures or discovery objectivesOutputs
MoleculesScores or posesScientific 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.
Induced-fit binding
Daniel E. Koshland Jr.Flexible docking, conformational selection, protein motion and ligand-induced pocket changes are modern extensions of this idea.
Multiscale modelling of chemical systems
Martin Karplus, Michael Levitt & Arieh WarshelQM/MM, molecular dynamics, free-energy methods and physics–ML hybrid platforms descend directly from this multiscale strategy.
Denoising diffusion generative models
Jascha Sohl-Dickstein, Jonathan Ho and collaboratorsModern protein-backbone, molecular-pose and biomolecular-complex generators use diffusion to sample valid three-dimensional structures and designs.
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.
Statistical mechanics and the Boltzmann distribution
Ludwig BoltzmannConformational ensembles, molecular simulations, temperature scaling, sampling and energy-based generative models rely on this statistical view.
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
Spun out of Caltech quantum-chemistry research.
NeuralPLexer models protein flexibility on ligand binding.