Explore h ow AI models predict protein 3D structures from sequences, enabling insights into folding pathways and functional conformations
Examine emerging co-folding models that reveal protein–protein interactions and guide multimeric complex design.

Learn how AI-driven approaches integrate multiomics data, including genomics, proteomics, and transcriptomics, to identify potential drug targets and disease biomarkers for complex diseases.
Explore how AI models synthesize cross-omic data and real-time multiomic information to uncover novel biological mechanisms, identify potential biomarkers and enable precision medicine.

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.

Learn how predictive simulations, generative AI and differentiating clinical biomarkers are forecasted to cut prototyping timelines by weeks and reduce per‑trial costs.

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Empowers strategic R&D with AI-driven predictive modelling early in discovery, fostering a data-driven culture.

Learn how  GenAI is transforming early drug discovery by designing novel, drug-like small molecules with improved potency, selectivity, and ADME properties.
Explore how GenAI integrates with synthesis planning and automation tools to prioritize viable candidates and accelerate iterative drug development.

Discuss how AI is used to identify pathological features, discover drug targets, and decode complex disease biology at a systems level.