AI service — AI Medical · Drug discovery
Molecular property prediction
Predict ADMET and other molecular properties from structure, embed molecules for your own models, and mine literature for drug interactions.
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.
Property prediction is the screening step that decides which molecules are worth making. These models take a structure — as SMILES, a graph or 3D coordinates — and return property predictions or an embedding a regressor of your own is trained on.
Structures arrive as a file or over your API and come back as predictions at tens of thousands of molecules per hour. That throughput is what makes virtual screening a cost line rather than a capacity problem.
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 molecular property 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
- Virtual screening — Filter a library on predicted ADMET before committing synthesis.
- In-house property models — Train on embeddings using your own assay data.
- Interaction mining — Extract drug-drug interaction statements from literature and labels.
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