Start with the decision, not the software.
BayesArc keeps a drug-development programme anchored to the next scientific decision. Open a real workflow, run qualified computation, inspect the underlying evidence and carry the result forward without losing provenance, assumptions or review context.
Continue real work already in progress.
This panel never fabricates programme metrics. It only renders a saved Discovery Campaign when BayesArc can resolve the persisted campaign identifier and live backend status.
What are you trying to decide?
BayesArc routes the question to the relevant scientific workspace. Clients should not need to know internal model names or choose between implementation components.
Is this target ready for a discovery campaign?
Define the programme, resolve experimental structures and inspect target-readiness blockers before compute starts.
Target & readiness 02What structure should we use or predict?
Work with sequence-to-structure prediction and carry the resulting structure into downstream structure-based analysis.
Structure workspace 03Which chemical ideas should enter screening?
Generate and filter real molecular candidates with chemistry constraints, deduplication and scaffold diversity before docking.
Generate molecules 04How do these candidates interact with the target?
Run real structure-based docking and inspect computational pose/ranking evidence without presenting scores as measured affinity.
Molecular docking 05Which candidates should advance?
Compare docking, developability, ADMET, novelty and synthesis evidence and prepare a reviewable candidate nomination.
Candidate decision 06What dose, exposure or development strategy should advance?
Continue the programme into model-informed drug development while preserving the assumptions and evidence that support the decision.
MIDD workspaceShow what can actually run now.
The status below is populated from BayesArc's public Scientific Capability Registry. Runtime availability remains separate from scientific qualification for a particular context of use.
Outputs remain evidence—not automatic decisions.
Retrieved, predicted and computed outputs remain explicitly classified. Experimental evidence and qualified human review remain distinct. BayesArc should make a method easier to inspect, not make uncertainty disappear.