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Explore how AI accelerates antibody discovery by enabling de novo design, epitope prediction, and in silico affinity maturation for highly specific, developable therapeutics.
Learn how deep learning and structure-based models optimize antibody stability, immunogenicity and target binding to advance precision biologics.

Moderator

Author:

Petar Pop-Damkov

Director
AstraZeneca

Petar Pop-Damkov

Director
AstraZeneca

Author:

Eli Bixby

CoFounder & Head of ML
Cradle

Eli makes sure Cradle's models and algorithms are doing what we think they are doing, and he keeps an eye out for the latest and greatest techniques in the literature. He was previously at Google (Brain, Accelerated Science, Cloud) working on biological sequence design, AutoML, and natural language understanding. He studied mathematics, computer science, and biochemistry

Eli Bixby

CoFounder & Head of ML
Cradle

Eli makes sure Cradle's models and algorithms are doing what we think they are doing, and he keeps an eye out for the latest and greatest techniques in the literature. He was previously at Google (Brain, Accelerated Science, Cloud) working on biological sequence design, AutoML, and natural language understanding. He studied mathematics, computer science, and biochemistry

Author:

Claudette Fuller

Vice President, Non Clinical Safety & Toxicology
Genmab

Claudette Fuller

Vice President, Non Clinical Safety & Toxicology
Genmab

Author:

Gevorg Grigoryan

Co-Founder & CTO
Generate Biomedicines

Gevorg Grigoryan

Co-Founder & CTO
Generate Biomedicines
  • Integrating AI and decision science to design faster, smarter, lower-risk trials
  • Using adaptive methods to boost efficiency and responsiveness in trial design
  • Turning complex data into insights that accelerate timelines and improve outcomes

Author:

Tom Oliver

Head of Product
Faculty

Tom Oliver is Head of Product at Faculty, where he leads the strategy and roadmap for Faculty Frontier™, the company’s decision-intelligence platform that helps enterprises operationalize AI in day-to-day decisions. Tom partners with executives and cross functional teams, including product managers, engineers, data scientists, and designers, to discover, build, and scale products that solve real problems for technical and business users. He collaborates closely with Faculty’s leadership and with key stakeholders and clients across pharma, retail, consumer goods, and healthcare, turning complex challenges into measurable outcomes. Before joining Faculty, Tom held leadership positions at PwC, focusing on technology enabled transformation, operating model change, and enterprise technology selection and deployment. His public writing explores the future of work, AI ethics, and human centric design, asking how powerful systems can advance human flourishing, not only efficiency. He holds a degree from the University of Oxford.

Tom Oliver

Head of Product
Faculty

Tom Oliver is Head of Product at Faculty, where he leads the strategy and roadmap for Faculty Frontier™, the company’s decision-intelligence platform that helps enterprises operationalize AI in day-to-day decisions. Tom partners with executives and cross functional teams, including product managers, engineers, data scientists, and designers, to discover, build, and scale products that solve real problems for technical and business users. He collaborates closely with Faculty’s leadership and with key stakeholders and clients across pharma, retail, consumer goods, and healthcare, turning complex challenges into measurable outcomes. Before joining Faculty, Tom held leadership positions at PwC, focusing on technology enabled transformation, operating model change, and enterprise technology selection and deployment. His public writing explores the future of work, AI ethics, and human centric design, asking how powerful systems can advance human flourishing, not only efficiency. He holds a degree from the University of Oxford.

Author:

Tarun Walia

Senior Director, Decision Science
Novo Nordisk

Tarun Walia

Senior Director, Decision Science
Novo Nordisk

Author:

Stephanie Vakaljan

Director of Trial Analytics and Decision Support
GSK

Stephanie Vakaljan

Director of Trial Analytics and Decision Support
GSK

Learn how AI models enhance physics-based simulations to predict molecular interactions and optimize drug design.
Discover the synergy between machine learning and classical methods to accelerate screening and improve the accuracy of drug discovery.

Author:

Sreyoshi Sur

Former Scientist, Molecular Engineering & Modeling
Moderna

Sreyoshi Sur

Former Scientist, Molecular Engineering & Modeling
Moderna

Explore how AI enhances biomarker discovery by analyzing large datasets to uncover novel biomarkers for disease diagnosis and therapeutic efficacy.
Learn how integrating digital biomarkers with AI improves the interpretation of data from wearable devices and traditional lab-based biomarkers for better patient stratification and treatment personalization.

Moderator

Author:

Nikolaos Patsopoulos

Biomarker Development Therapeutic Area Lead
Novartis Institutes for BioMedical Research (NIBR)

Nikolaos Patsopoulos

Biomarker Development Therapeutic Area Lead
Novartis Institutes for BioMedical Research (NIBR)

Author:

Satarupa Mukherjee

R&D Leader, AI/ML (Digital Pathology)
Roche

Satarupa Mukherjee

R&D Leader, AI/ML (Digital Pathology)
Roche

Author:

Jack Geremia

CEO
Matterworks

Jack Geremia

CEO
Matterworks

Author:

Virginia Savova

Senior Director, Head Cell-Targeted Precision Medicine
AstraZeneca

Virginia Savova

Senior Director, Head Cell-Targeted Precision Medicine
AstraZeneca

Examine how AI models are being developed, validated, and governed to meet regulatory expectations, with practical insights into documentation, auditability, and lifecycle management to ensure safe, transparent, and compliant deployment in GxP environments.

Author:

Benjamin Stevens

Director, CMC Policy
GSK

Benjamin Stevens

Director, CMC Policy
GSK
  • Explore how AI models predict protein 3D structures from sequences, enabling insights into folding pathways and functional conformations
  • Examine foundational models that reveal protein–protein interactions and guide design of innovative drug candidates 

Author:

Miles Congreve

Chief Scientific Officer
Isomorphic Labs

Miles Congreve

Chief Scientific Officer
Isomorphic Labs