20+ Presentations from Top Biopharma at PEGS AI
Beyond the Funnel: Machine Learning-Powered Lab-in-the-Loop for Drug Discovery
Richard A. Bonneau, PhD, Vice President, Drug Discovery, Prescient Design, a Genentech Co.
Accelerate Early-Stage Drug Discovery with a First-of-Its-Kind Collaborative Platform
Vanessa Braunstein, Senior Director, TuneLab AI Drug Discovery Platform, Eli Lilly and Company
Hao Zheng, Senior Director, AI/ML Product Lead, Eli Lilly
Closing the Loop: Building the Infrastructure, Models, and Automated Labs for AI-First Biologics
Simon Chell, PhD, Vice President, Biologics Engineering, AstraZeneca
Distilling Energetic Scores into an Antibody Binding Prediction Model
Bowen Dai, PhD, Advisor, Computational Biology, Eli Lilly and Company
FIRESIDE CHAT: AI's Real Impact on Biologic Drug Discovery: The Honest Scorecard
Moderator: Kristine Deibler, PhD, Director, Molecular Artificial Intelligence, Novo AS
Co-Folding: Accurate Structure Prediction of Large Molecules
Frédéric Dreyer, PhD, Principal ML Scientist, Prescient Design, Genentech
Conquering Huge Design Spaces: AI-Driven Developability for Next-Generation Biotherapeutics
Norbert Furtmann, PhD, Head, Biologics AI & Design, Computational and AI Strategy, Sanofi
Transforming Biologics Discovery in the Era of AI
Athena Hadjixenofontos, PhD, Director, Data Science & Head of AI/ML, Biotherapeutics and Genetic Medicines, Discovery Research, Abbvie
Closed-Loop Integration of Immune Repertoires and Generative Models for Discovery of Lead-Quality Antibodies against Challenging Targets
Zhao Huang, PhD, Principal Research Scientist II, Protein Therapeutics, Gilead Sciences Inc
Automating Therapeutic Epitope Selection with Agentic AI Workflows
Cody Krivacic, PhD, Principal Scientist, Machine Learning & Computational Protein Design, GSK
Leveraging AI and Data to Boost Efficiency in Biologics Discovery
Yuan Lin, Director Digital Biologics Products, Pfizer Inc.
Developability by Design: Integrating in silico and Experimental Data for Antibody Engineering
Lasse Møller Blaabjerg, PhD, Data Scientist, Discovery Data Science, Genmab
AI/ML-Enabled Developability Prediction for Antibody Therapeutics and Multi-Specifics
Olga Obrezanova, PhD, AI Principal Scientist, Biologics Engineering, Oncology R&D, AstraZeneca
Rethinking Design Objectives in the Path from Computational Biologics to the Clinic
Jorge Roel-Touris, PhD, Head, Biologics Design, Digital Biologics Platform, Large Molecules Research, Sanofi
Prediction of Antibody Non-Specificity Using Protein Language Models
Laila Sakhnini, PhD, Principal Scientist, Biophysics & Injectable Formulation, Novo
Beyond the Frontier: Unlocking Real-World Portfolio Impact with AI for Molecule Drug Discovery
Franziska Seeger, PhD, Senior Director, AI for Drug Discovery, Genentech Inc.
Lessons from de novo Design of Antibodies: Benchmarks, Inflection Points, and Common Failure Modes
Roberto Spreafico, PhD, Senior Director, Biologics AI Innovation, AstraZeneca
The Next Era of Drug Discovery: De novo Design of Biologics
Anna Vangone, PhD, Director of AI/ML Large Molecule for Drug Discovery, Computational Science Center of Excellence, Roche/Genentech
How Active Learning Can Help Identify the Right Sequences for Optimal Model Performance
Michail Vlysidis, PhD, Principal Engineer, AbbVie
Protein Language Models for Antibody Developability, Prediction, and Optimization
Ye Wang, PhD, Principal Scientist, Machine Learning, Biogen
Unlocking Challenging Targets: Drug Discovery via Immune Repertoire Analysis
Yulei Zhang, PhD, Senior Advisor, Eli Lilly and Company
* As of 9/29/26. Please see individual agenda pages for most up-to-date agenda.