Explore how AI accelerates the design of complex biologics, including ADCs and engineered cell therapies.
Learn how predictive models improve developability by forecasting linker stability, payload efficacy, and manufacturability.

Explore how AI-driven digital twins and functional models integrate patient-specific biology to identify and validate high-confidence drug targets by simulating system-level responses to genetic or pharmacological perturbations.
Learn how perturbation modelling with multiomic and functional genomics data predicts the effects of interventions on disease pathways, while LLMs synthesize data to uncover and prioritize novel therapeutic targets.

Equip teams with AI tools that capture process knowledge and simulate scale-up scenarios, reducing tech transfer timelines and improving first-batch success rates - critical for aligning R&D, MSAT, and manufacturing expectations early.

Gain actionable strategies for embedding generative AI and large language models into early-phase trial design and execution, from protocol drafting and site selection to patient engagement, accelerating timelines while ensuring data quality and compliance

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.