AI service — AI Medical · Drug discovery
Binding affinity prediction
Co-fold protein and ligand together, predict binding affinity, and rank docking poses — at a fraction of physics-based compute.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
Affinity is the number that decides whether a hit is worth pursuing, and the physics-based methods that estimate it well are too slow to run at library scale. These models co-fold the complex and predict affinity directly, which moves that estimate from a few compounds to a campaign.
Protein sequence and ligand structure arrive over your API and come back as a predicted complex, a confidence score and, where supported, an affinity estimate. This is batch work that suits scheduled cluster capacity.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the binding affinity prediction pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Hit triage — Rank a hit list by predicted affinity before committing assay capacity.
- Pose prediction — Generate and rank ligand poses against a target structure.
- Complex modelling — Co-fold protein with ligand, DNA or RNA in one pass.
More Drug discovery services
- Genomic sequence analysisGenomic sequence analysis service →
- Single-cell analysisSingle-cell analysis service →
- Protein structure predictionProtein structure prediction service →
- Molecular property predictionMolecular property prediction service →