AIDDD Agenda

Key Takeaways: 

• We have developed technology that efficiently applies predictive ML models to combinatorial synthesis libraries (CSLs) at a rate of ~500 B compounds per second, per GPU, allowing us to exhaustively evaluate otherwise enumerable chemical space to identify diverse compounds that best fit multi-parameter optimization criteria.
• In practice, this gives chemists the opportunity to flexibly and iteratively explore enormous chemical space while receiving results, in the form of enumerated chemical structures, within seconds of their inquiry, fostering a genuine creative process that leads to higher chemical diversity and developability.
• We use this approach in conjunction with our next-generation extrapolative ML models, which has allowed us to discover high-quality, diverse, structurally-distinct binders for targets of interest; however, this approach can be extended to any advanced predictive model in order to exhaustively evaluate any CSL.

Gain actionable strategies for embedding AI and large language models into portfolio decision making, accelerating timelines while ensuring data quality and compliance

Explore how knowledge graphs integrate multi-source biological data, such as genetic, proteomic, and clinical information, into unified models that accelerate target discovery and disease understanding, with AI enhancing the extraction of actionable insights.
Learn how data normalization and the latest curation strategies ensure that biological datasets are clean, standardized, and AI-ready, enabling accurate analysis and improved model performance for drug development.

Discover practical strategies for scaling Process Analytical Technology (PAT) from R&D into regulated GMP environments , including method validation, data integrity, and cross-functional alignment to ensure continuity, compliance, and control at commercial scale.

Explore how AI-powered single-cell and spatial biology technologies reveal cellular heterogeneity, tissue organization, and microenvironmental interactions to uncover disease mechanisms and therapeutic targets.
Learn how AI models analyze high-dimensional cellular and spatial data to define pathogenic cell states, map dysregulated pathways, and prioritize targets for early-stage therapeutic discovery.

Demonstrate how AI-driven initiatives - like predictive modelling and automated inspection -translate into measurable outcomes (e.g., defect reduction, shorter batch release cycles) that justify capital investment and cross-functional prioritization