Session Abstract – PMWC 2025 Silicon Valley
Track 2
Track Chair: Janusz Dutkowski, Data4Cure
Session: Knowledge Graphs, LLMs & Foundation Models for Enhanced Drug Development
This session will discuss how AI can combine different types of data to improve drug development. Case-studies will span a range of AI technologies (including LLMs and foundation models for omics data, generative AI approaches and knowledge graphs) and their applications from early stage R&D to clinical trials. We will showcase examples of successful projects and collaborations that are making an impact across drug discovery and development pipelines.
Speaker Profile
Ph.D., CEO & Founder, Data4Cure
Biography
Janusz Dutkowski is Founder and CEO of Data4Cure, Inc. a company focused on combining systems biology, machine learning and AI to facilitate continuous translation of biomedical data to knowledge. His background is in mathematics and computer science and he has been working at the intersection of mathematics, computer science and biology to develop new data driven technologies to advance the discovery of new biomarkers and precision based therapies. His research contributions include the development of methods for multiscale analysis of molecular networks and integrative methods for biomarker discovery from multidimensional data. He has co authored over 20 research papers published in scientific journals including Nature Biotechnology, Science and Cell.
Speaker Profile
Ph.D., Vice President, Discovery Technologies, Pfizer
Biography
Dr. Rejto leads the development and integration of cutting-edge technologies in drug discovery. With over two decades of experience, he oversees high-throughput screening, structural biology, and computational drug discovery, focusing on enhancing the efficiency and effectiveness of Pfizer's research and development efforts.Dr. Rejto holds a Ph.D. from the University of California, Berkeley, and completed a postdoctoral fellowship at Harvard University. His leadership has advanced the integration of AI and machine learning in drug discovery, contributing to key therapeutic developments. Dr. Rejto is also an active contributor to the scientific community, with numerous publications and patents.
