Discuss how Lab in the Loop is revolutionizing drug discovery by integrating AI with experimental workflows, enhancing speed and accuracy in data collection and analysis.

This session provides the unique opportunity to listen to, and engage with, some of the most innovative AI Drug Discovery and Development start-ups globally. Focusing exclusively on early-stage funding, six startups picked by our esteemed selection committee will take to the stage in front of 100+ potential partners. Through a series of rapid-fire presentations, these pioneers will demonstrate their vision of the future of drug discovery, and how their product, technology, or service fits into it.

Highlight how digital twins and hybrid ML models (e.g., Bayesian, predictive) enable virtual experimentation and proactive troubleshooting, reducing scale-up failures and supporting more reliable process performance at commercial scale.

See how AI‑powered feasibility assessments and automated start‑up workflows can slash administrative cycle times, freeing your team to focus on critical clinical oversight.

Bolsters innovation agility by embedding ML Ops practices, aligning data science and IT workflows to ensure reliable, scalable AI deployments and a culture of continuous improvement.

Understand how AI forecasts reaction outcomes to streamline synthetic planning.
See how reaction prediction models minimize experimental trial and error.

Understand how AI can be used to optimize biologic drug design, particularly in antibody engineering and protein structure prediction.

Explore how ML-enabled real-time control systems and continuous process verification improve yield predictability, reduce rework, and enable faster release - offering a direct line of sight to cost savings and product quality gains.

Showcasing generative models that craft hyper‑personalized outreach messages and informed consent materials, driving up engagement rates and shaving weeks off recruitment timelines.
Discover how ML‑driven forecasts for recruitment rates and optimized site selection translate into faster first‑patient‑in and lower screen‑fail/dropout rates, saving you both time and budget.