Webinar 3: Building Scalable and Efficient Drug Discovery Workflows

This three-part webinar series explores how cutting-edge AI, including foundation models, is being applied to drug discovery. But we’re going beyond model performance to dig into the messy, essential work of infrastructure, data, team alignment, and use cases.

Whether you're building models, managing data platforms, or setting the strategic direction for AI initiatives, this series will arm you with practical insights from industry leaders actually deploying AI in their pipelines.

Key Takeaways Across the Series:

• Understand what’s needed beyond the model to deploy AI tools in complex R&D environments
• Explore how foundation and generative models are changing drug discovery
• Discover where teams are finding success in bridging data infrastructure and biological insight
• Gain practical tips on integrating AI workflows into legacy or siloed research ecosystems

Webinar 3: Building Scalable and Efficient Drug Discovery Workflows

Tuesday 21st October 2025 at 3pm BST / 4pm CEST / 10am ET

AI is transforming how pharma generates insights, accelerates pipelines, and delivers impact across R&D, but moving from proof-of-concept to tangible value requires more than models. This webinar brings together industry leaders to show how scalable AI infrastructure, multi-modal data integration, and alignment between science and business are driving discoveries at the bench and measurable impact. We’ll explore what it takes to build scalable AI systems, harness multi-modal datasets, and implement strategies for widespread, reproducible adoption.

• From Lab to Launch: Building Scalable AI Infrastructure for Scientific Discovery and Business Impact - Nastaran Heidari, Principal Data and AI Product Lead, Formerly Pfizer
• Harnessing AI Infrastructure for Multi-Modal Analysis of the APOLLOE4 Phase 3 Study in APOE4/4 Homozygotes with Early Alzheimer’s - Adem Albayrak, Chief Data Officer, Alzheon
• Assessing Foundation Models for Single-Cell and Multimodal Analysis: Promise, Performance, and Infrastructure Realities - Gaurav Kumar, Associate Director, Computational Biology, Teva Pharmaceuticals

Available On-Demand

Webinar 1: Beyond the AI Models: What It Really Takes to Deploy AI for Drug Discovery

Many questions remain about how to truly operationalize AI in drug discovery. It begins with clean, standardized data, robust pipelines, and the right tools and governance. Real success comes from bridging leadership objectives with practical challenges and breaking down silos across technology, biology, and business teams. This discussion cuts through the hype to explore what it really takes to embed AI into R&D—from ensuring data quality and building resilient infrastructure to fostering cross-functional collaboration and establishing strong governance frameworks.

Hear from:

• Joe Szustakowski, VP, Clinical Development and Data Science, Genmab
• Marcin von Grotthuss, Director, Head of AI/ML Enablement and Modeling, Takeda
• Nicola Bonzanni, Founder and CEO, ENPICOM

Sponsored by Enpicom

Webinar 2: Linking AI Computational Models to Drug Candidates

With AI-driven molecular design transforming drug development, this webinar asks a critical question: Can algorithmically generated molecules truly become reliable, synthesizable, and impactful drug candidates? Join us to explore how AI models are applied in pipelines, how human experts curate and validate outputs, and what strategies are working to overcome common pitfalls—from ensuring biological relevance to delivering tangible outcomes in exposure modeling, IP mapping, and MOA analysis.

• Accelerating Drug Exposure Analysis with Machine Learning - Sam Richardson, Associate Director ML/AI, AstraZeneca
• AI-Powered Patent Mining for In Silico Oligonucleotide Drug Design - Dimitar Yonchev, Data Engineer, Roche
• AI-Driven Patient-Response Simulations in Oncology Using TCGA Omics and Transformers - Ted Hong, Associate Director, Bioinformatics, AstraZeneca