Technology
Models are only useful if you can interrogate them
Every buyer has heard the AI pitch, and most have been disappointed by at least one vendor. So rather than describe our architecture, here is how we work - which is the part that determines whether the output is worth anything.
Transparent methodology
Every engagement delivers the parameter record, the model configuration and the decision logic behind the ranking. If you cannot inspect why a target or compound scored as it did, you cannot defend the decision internally - and we would not expect you to make it.
Benchmarked, not asserted
Model performance is stated against benchmarks and reference datasets, with the failure modes described. We would rather tell you where an approach is weak than discover it together at the bench.
Validation is part of the method, not a separate service
The reason our computational work is worth buying is that we test it. Predictions carry acceptance criteria agreed before work starts, and confirmation runs in the same contract.
Reproducibility
Pipelines are versioned and containerised where appropriate, with the full parameter record delivered. Your analysis can be repeated - by you, by a reviewer, or by a regulator - without us.
Capability areas
- Target identification and prioritisation
- Structure- and ligand-based virtual screening
- Molecular dynamics and free-energy calculation
- QSAR and generative design for lead optimisation
- ADMET and liability prediction
- Multi-omics integration
- Variant annotation and classification
- Clinical decision support
Related
Where to read next
Enterprise AI for Life Sciences
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