Explore how AI-driven approaches enhance high-throughput screening by optimizing DNA-encoded libraries (DEL) for rapid identification of potential drug candidates.
Learn how AI algorithms accelerate the analysis of complex screening data, enabling more efficient lead discovery and targeting of molecular interactions.

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.

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.

Discover how ML and active learning techniques are revolutionizing the search for promising drug candidates in vast chemical libraries, accelerating hit identification.
Learn how AI models navigate ultra-large chemical spaces, prioritize bioactive compounds, and streamline the discovery of potential hits for further development.

Explore how AI accelerates antibody discovery by enabling de novo design, epitope prediction, and in silico affinity maturation for highly specific, developable therapeutics.
Learn how deep learning and structure-based models optimize antibody stability, immunogenicity and target binding to advance precision biologics.

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.