2026 PLENARY KEYNOTE SESSION:
TRENDS AND INNOVATION DRIVING THE FUTURE OF BIOTHERAPEUTICS
(Shared with Co-Located PepTalk)

Chairperson’s Remarks
John K. KawooyaJohn K. Kawooya, PhD, Private Consultant, Robotics-Plate-Based-Ultra-HT Biologics Purification


Building an AI-Native Platform for Accelerated Biologics Discovery at Sanofi
Yves FomekongYves Fomekong Nanfack, PhD, Executive Director & Head, End-to-End AI Foundations, Large Molecules Research Platform, Sanofi


Agentic AI for Biologics: Scalable Infrastructure for GxP-Compliant, Insight-Driven Testing
Lieza M. DananLieza M. Danan, PhD, Co-Founder & CEO, LiVeritas Biosciences


Technological Trends Shaping the Landscape of Biopharmaceuticals
Aline OliveiraAline de Almeida Oliveira, PhD, Competitive Intelligence Office (AICOM), Bio-Manguinhos/Fiocruz, Brazil



PLENARY FIRESIDE CHAT
Moderator:
John K. Kawooya, PhD, Private Consultant, Robotics-Plate-Based-Ultra-HT Biologics Purification
Panelists:
Yves Fomekong Nanfack, PhD, Executive Director & Head, End-to-End AI Foundations, Large Molecules Research Platform, Sanofi
Lieza M. Danan, PhD, Co-Founder & CEO, LiVeritas Biosciences
Aline de Almeida Oliveira, PhD, Competitive Intelligence Office (AICOM), Bio-Manguinhos/Fiocruz, Brazil


2026 PLENARY KEYNOTE SESSION
(Shared with Co-Located PepTalk)

Plenary Keynote Introduction
Andrew Nixon, PhD, Senior Vice President, Global Head Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc.

New Frontier of Biotherapeutic Discovery: Where Machine Learning Meets Molecular Design
Stephanie Truhlar, PhD, Vice President, Biotechnology Discovery Research, Eli Lilly and Company



PLENARY FIRESIDE CHAT: End-to-End in silico-Designed Biologics
Moderator:
Andrew NixonAndrew Nixon, PhD, Senior Vice President, Global Head Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc.


Panelists:
Charlotte M. DeaneCharlotte M. Deane, PhD, Professor, Structural Bioinformatics, Statistics, University of Oxford; Executive Chair, Engineering and Physical Sciences Research Council (EPSRC)
Garegin PapoianGaregin Papoian, PhD, Co-Founder & CSO, DeepOrigin
Stephanie TruhlarStephanie Truhlar, PhD, Vice President, Biotechnology Discovery Research, Eli Lilly and Company

 


  • How is the path to drug development different with ML/AI?
  • How far off is de novo design for biologics? For antibodies?
  • How is ML/AI used for target selection?
  • How do you accelerate DMTA cycles? 
  • Data standardization—how to incorporate historical data?
  • Federated learning—how do you ensure you have enough data to build a model?
  • Promoting change management

2026 KEYNOTES

Talk Title to be Announced
Peyton GreensidePeyton Greenside, PhD, Co-Founder & CSO, BigHat Biosciences



Panel Discussion: Building Multi-Scale and Multi-Modal Models
Moderator:
Winston HaynesWinston Haynes, PhD, Vice President, Computational Sciences and Engineering, LabGenius Therapeutics



Panelists:
Qing ChaiQing Chai, PhD, Research Advisor, Biotechnology Discovery Research, Eli Lilly and Company
Peyton GreensidePeyton Greenside, PhD, Co-Founder & CSO, BigHat Biosciences
Jeremy WohlwendJeremy Wohlwend, PhD, CTO, Boltz



Chairperson
Hunter ElliottHunter Elliott, PhD, Senior Director, Machine Learning, BigHat Biosciences



Incorporating in silico Tools into Antibody Discovery: Challenges and Opportunities
Andrew NixonAndrew Nixon, PhD, Senior Vice President, Global Head Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc.


Talk Title to be Announced
Charlotte M. DeaneCharlotte M. Deane, PhD, Professor, Structural Bioinformatics, Statistics, University of Oxford; Executive Chair, Engineering and Physical Sciences Research Council (EPSRC)


Redesigning Antibody CDRs to Improve Developability Properties Using Machine Learning 
Peter M. TessierPeter M. Tessier, PhD, Albert M. Mattocks Professor, Pharmaceutical Sciences & Chemical Engineering, University of Michigan



* As of August 20. Please see individual agenda pages for most up-to-date agenda.


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Event-At-a-Glance

Data Strategies and the Future of AI Models