Traditional suites are powerful toolkits, but they are static, do not respond to the context of a drug discovery program, and expect every user to know which algorithm to run and when.
Blaise AI is a medicinal-chemistry agent: it orchestrates multiple modelling tools, chooses the right level of theory based on model uncertainty and expected value, and crucially learns from medicinal chemist feedback.
It is less a toolbox and more a collaborative co-pilot that continuously improves as your team interacts with it.

Machine-learning models with calibrated uncertainty estimates score candidate molecules. A built-in expected-value engine directs heavy computations such as free-energy perturbation or quantum calculations only to compounds where the extra accuracy is likely to drive project decisions. This keeps compute usage efficient without sacrificing scientific rigour.

Large language models are used for natural-language interaction, automated documentation parsing and tool orchestrations. Domain-specific models such as graph neural networks and physicochemical models are used for property prediction. Statistical methods are used for calibrated uncertainty estimation.

Blaise AI was built for the reality of imperfect data. Its models use uncertainty estimation, automatically weighting predictions by confidence. This means unreliable data points do not disproportionately influence design decisions.

No. The interface is natural-language driven. Chemists interact by asking questions or providing simple likes/dislikes on suggested molecules. There is no need to learn command-line tools or scripting; the system reads the documentation of underlying methods and translates chemist intent into the correct computation.

Chemists’ feedback (e.g. “hard to synthesise”, “ugly polar scaffold”) is treated as a signal, not ground truth. It is combined with empirical assay data and aggregated across users to avoid single-person bias. This allows the platform to capture practical medicinal chemistry intuition while still letting experimental results guide final optimisation.

Yes. The platform has been built with a modular setup. Your in-house QSAR models, compound registry, docking engines, or proprietary scoring functions can be deployed and orchestrated by Blaise.

Feedback is aggregated and versioned.
The system captures each chemist’s input separately and uses statistical weighting to identify areas of consensus or persistent disagreement.

Every interaction including likes, dislikes and notes feeds a continuous learning loop. Models are retrained incorporating both new experimental data and chemist preference signals. This results in progressively sharper generative designs and prioritisation accuracy.