Beyond the AI models: What it really takes to deploy AI for drug discovery
Everyone talks about the models. Almost no one talks about the infrastructure, data, workflows, and organizational alignment required to make AI actually work in biologics R&D.
This webinar panel is part of the Front Line Genomics series Transforming Drug Discovery with AI: From Models to Mechanisms to Scalable Impact. The session brings together experts from Genmab, Takeda, and ENPICOM to outline what it truly takes to operationalize AI: from high-performance data foundations to cross-functional adoption.
Discussion topics
- How to build the data foundations, pipelines, and governance needed to make AI usable and scalable in biologics discovery and engineering
- How to navigate the tension between leadership ambitions and scientific or engineering constraints
- Practical strategies to bridge gaps between technical, operational, and executive teams when deploying AI
- The importance of data homogenization and the standardization of data analysis pipelines
Meet the panelists
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
Beyond the AI models: What it really takes to deploy AI for drug discovery
Relevant resources
White paperAI in biologics discovery and engineering: A practical guide to driving adoption
Discover how AI can transform biologics discovery and overcome challenges like fragmented workflows, infrastructure limitations, and underutilized models.
Press releaseENPICOM joins the Pistoia Alliance
Membership deepens ENPICOM’s commitment to advancing AI and autonomous agent infrastructure for life sciences R&D.
WebinarLibrary to Lead: Rapid In-Lab Sequencing Technologies for Expanding Antibody Discovery
Illumina, Twist Bioscience, and ENPICOM explore an end-to-end workflow for accelerating scFv discovery — from sequencing to synthetic library design to AI-driven analysis.