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Experiments that improve the model
Wet-Lab Validation Loops
BioForge is built around the idea that therapeutic discovery improves when computational reasoning and wet-lab validation are planned together. The platform helps define assay priorities, evidence gaps, controls, readouts, and next design decisions so each experiment updates the discovery program rather than becoming a disconnected report.
Translate AI-generated hypotheses into practical validation experiments.
Use assay results to update target, molecule, and modality priorities.
Maintain a traceable connection between design rationale and lab evidence.
Problem context
Why this problem matters
Assay scarcity
Every validation slot has opportunity cost, so experiments should be chosen for the information they add to the discovery program.
Learning loops
Wet-lab results should update hypotheses, candidate rankings, uncertainty, and the next computational cycle.
Partner execution
BioForge is designed to work with pharma, biotech, CRO, and academic lab partners that own different parts of validation.