When the data meets the lab
06 May 2026
Lead Generation & Optimization
- Closing the loop: active learning in practice. What does a truly functional wet lab / AI feedback cycle look like, and where do most organizations fall short? How do we design experiments that are maximally informative to the model, not just scientifically interesting?
- Data quality vs. data quantity in antibody optimization. As teams scale up AI-driven sequence generation, what assay choices and experimental designs generate the highest-signal training data? How do we avoid encoding lab artifacts and noise into the next model iteration?
- Defining the right fitness function. Binding affinity is easy to measure — developability, selectivity, and immunogenicity are not. How do teams balance optimizing for what's measurable versus what ultimately matters for a clinical candidate?
- Navigating sequence space responsibly. AI models can propose enormous libraries of candidate sequences. What frameworks are teams using to triage and prioritize for synthesis and screening — and how do you keep biologists meaningfully in the loop rather than just executing a computational queue?
- Failure modes no one talks about. When an AI-guided campaign underperforms, is it a data problem, a model problem, or an experimental design problem? What have teams actually learned from campaigns that didn't work as expected?
- Organizational and cultural readiness. Beyond the algorithms, what structural changes — in team composition, incentives, and decision-making — are necessary for AI-driven lead generation to actually change how drug discovery operates, rather than becoming a sophisticated but marginal tool?
Industry Expert
