Equip yourself with KPI dashboards and financial models to quantify time‑to‑value, optimize resource allocation, and build a compelling business case for AI investment.

Forges powerful ecosystems by aligning pharma, tech, and academia, enabling shared expertise and resources to accelerate breakthroughs and navigate complex R&D challenges.

Explore how machine learning techniques, such as supervised learning and deep learning, predict critical ADME properties like solubility, permeability, and DDI risk.
Discover how computational methods, including molecular docking and quantum chemistry simulations, optimize high-affinity drug-target interactions for enhanced efficacy.

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.

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 generative AI is being used to analyze real-world data at scale, enabling earlier signal detection, automated safety reporting, and more dynamic risk-benefit monitoring, driving smarter, faster post-market decision-making across the product lifecycle.

Safeguards the innovation pipeline by proactively securing sensitive research data, enhancing risk resilience and ensuring stakeholder confidence in the integrity of AI-driven discoveries.

Explore how AI and large language models are revolutionizing reaction prediction, retrosynthesis planning, and synthetic accessibility scoring.
Learn how to evaluate and optimize AI-generated leads for real-world developability, including solubility, stability, and synthetic tractability.

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.

Hear cross-functional perspectives on successfully implementing AI across process development teams, from aligning with quality, IT, and manufacturing to overcoming cultural and technical barriers, with a focus on driving operational efficiency and long-term value.