Translating real-World & multi-omic data into target prioritization for drug discovery
09 Sept 2026
Drug Design & Modeling
- Signal vs. Noise: How do we effectively integrate messy, diverse Real-World Data (RWD) with highly variable multi-omic datasets without drowning out true biological signals?
- Causality over Correlation: What modeling frameworks are proving most reliable for separating true therapeutic drivers from innocent "bystander" biomarkers?
- The Dry-to-Wet Lab Gap: What specific evidence or confidence thresholds must a computationally derived target meet before wet-lab biologists trust it enough to invest in validation?
- Early Stratification: How can we leverage RWD earlier in target selection to predict drug efficacy for specific patient sub-populations, rather than waiting for late-stage clinical trial failures?
Industry Expert
