What it is
Numerion Labs is the current identity of the company formerly known as Atomwise. Its present chemistry platform describes COSMOS for chemistry foundation modelling, APEX for exhaustive ultra-large-library enumeration and EXPO for project-specific model optimization. AtomNet is retained as a legacy model lineage rather than represented as a separate active company.
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 Numerion AI Chemistry Platform 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 →1 connected frontier
Uncertainty-aware screening · Recent preprint
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.
Fast, uncertainty-aware affinity screening
Terray Therapeutics research team · 2026-02-08Can virtual screening retain useful structural and affinity accuracy without expensive all-atom diffusion for every compound?
Evidence boundary and unresolved questions
The results are preprint claims; proprietary assay details and cross-lab prospective replication remain limited.
- Does coarse representation preserve water, ion, metal and covalent chemistry?
- How well does uncertainty calibrate under target and chemistry shift?
- Can active learning improve real design-make-test cycles?
virtual screening · uncertainty · active learning · coarse representationOpen 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.
Quantitative structure–activity relationships
Corwin HanschClassical QSAR established the central premise that molecular features can predict potency and guide optimization—the conceptual ancestor of modern molecular machine learning.
Selective toxicity and the ‘magic bullet’
Paul EhrlichTarget selectivity, therapeutic index and mechanism-based screening remain central goals of drug discovery.
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.
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.
Transformer self-attention
Ashish Vaswani and colleaguesProtein, genome, molecule and single-cell foundation models use attention to learn dependencies across biological sequences and multimodal inputs.
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
Formerly known as Atomwise.
The current provider platform names COSMOS, APEX and EXPO; AtomNet remains historical lineage.