What it is
Absci combines generative AI with very-high-throughput synthetic biology to design antibodies de novo — creating candidates for a specific target and epitope rather than screening existing libraries. Its closed loop of AI design and lab measurement aims to compress antibody discovery timelines.
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 Absci — Integrated Drug Creation 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
Complex biologics · 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.
High-fidelity antibody and biologic interfaces
Isomorphic Labs · 2026-02-10Can general co-folding models accurately resolve antibody–antigen and other biologic interfaces with low sequence homology?
Evidence boundary and unresolved questions
Benchmark composition, success thresholds and independent reproduction will determine how broadly the reported advantage generalizes.
- How does performance vary across CDR loops, nanobodies and multispecific formats?
- Can interface prediction improve prospective affinity maturation?
- How are glycosylation and conformational heterogeneity handled?
antibodies · biologics · interfaces · low homologyOpen frontier record →Inputs and outputs
Inputs
Antigen, sequence or desired propertiesOutputs
Antibody candidatesAffinity or developability estimatesScientific 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.
Selective toxicity and the ‘magic bullet’
Paul EhrlichTarget selectivity, therapeutic index and mechanism-based screening remain central goals of drug discovery.
Hybridoma production of monoclonal antibodies
Georges J. F. Köhler & César MilsteinTherapeutic antibodies, diagnostic antibodies and antibody discovery platforms became scalable and reproducible.
Somatic gene rearrangement generates antibody diversity
Susumu TonegawaAntibody language models and repertoire design operate on the sequence space created by V(D)J recombination and somatic diversification.
Phage display and selection of binding proteins
George P. Smith & Sir Gregory P. WinterDisplay-based selection created an experimental search engine for protein binders and remains a core validation partner for computational antibody design.
Cooperative ligand binding
Archibald V. HillDose–response curves, receptor occupancy, multisite binding and systems pharmacology still use Hill-type models.
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.
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
Publicly traded AI-biotech.
Reported zero-shot de novo antibody generation.