Reinforces trust and compliance by embedding robust data governance frameworks and ethical AI practices, to responsibly harness data and mitigate future regulatory risks.

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.

Active deep learning offers a promising approach for hit discovery starting from limited data by iteratively updating and improving models during screening by applying new data and adapting decisions. Key open questions include how best to explore chemical space, how it compares to non-iterative methods, and how to use it under data scarcity. We present ChemScreener, a multi-task active learning workflow for early drug discovery across large, diverse libraries or chemical spaces. Its Balanced-Ranking acquisition strategy leverages ensemble uncertainty to explore novel chemistry while maintaining hit rate enrichment by prioritizing predicted activity. In five iterative single-dose HTRF screens on WDR5 protein, ChemScreener increased hit rates from 0.49% (primary HTS screen) to 3–10% (average 5.91%; 104 hits from 1,760 compounds). Hits were consolidated, retested with close analogs together in the 269 compounds set and clustered; 44 hit compounds from 81 clusters of 269 compounds set advanced to dose–response and filtered by counter HTRF assays. Over 50% of those with IC50 < 45 μM were validated as WDR5 binders by DSF. We de novo identified three scaffold series and three singleton scaffolds as the hits. Overall, we demonstrated that ChemScreener can accelerate early hit discovery and yield more diverse chemotypes.

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.