Inform therapeutic strategy
Uncover potential targets by exploring biological connections between proteins, pathways, disease and other entities through a self-assembling systems biology view of experimental evidence.
Prioritize targets
Triage thousands of potential genes to select the most viable targets to pursue with a comprehensive picture of their linkages in the underlying biology of disease.
Validate hypotheses
Uncover scientific evidence to support prioritized targets and mechanisms of action.
Uncover lynchpin experiments
Eliminate unnecessary trial and error experimentation with an AI-augmented experiment path, defining the handful of key experiments that validate or negate the hypothesis, or allow early termination of pursuit.
Inform, troubleshoot and optimize experiment design
Assemble each experiment to yield a definite result by building each assay based on the ideal material and methods that have been proven to work in the desired experiment context.
Select proven reagents and model systems
Reduce irreproducibility and enhance likelihood of success by selecting reagents, protocols, model systems, techniques that have been proven to work in the desired experimental context.
Flag safety and efficacy risks
Have a clear understanding of risk profiles that would lead to adverse events with mechanisms of action across cascades of proteins and pathways, allowing early termination or compensatory strategies from the start.
Identify novel biomarkers
Discover predictive, prognostic, safety, and surrogate endpoint biomarker candidates. Uncover the underlying biological mechanisms and related methodologies to advance the therapeutic relevance of your research.
20-25% efficiency on experimental reagent spend.
An average of 1-1.5 years of acceleration per R&D program.
25% of projects identified a new indication to explore or an additional target gene not previously considered.
33% of projects identified a safety or efficacy risk early in the project lifecycle.