Paul Rejto
Vice President - Discovery Technologies Pfizer
Paul Rejto is Vice President of Discovery Technologies at Pfizer, where he leads teams developing and applying innovative technologies to identify therapeutic targets and advance first-in-class oncology candidates into clinical development. With more than three decades at Pfizer and Agouron Pharmaceuticals, he has held leadership roles spanning discovery technologies, translational research, precision medicine, computational biology, and structure-based drug design. As a core member of Pfizer’s Oncology R&D leadership, Paul provides strategic oversight across the oncology portfolio from target identification through proof of concept. His expertise lies in leveraging computational and translational sciences to accelerate the development of transformative cancer therapies.
Seminars
AI and machine learning are rapidly becoming embedded within structural biology and drug discovery, but the research community is increasingly asking a more practical question: which approaches genuinely improve SBDD outcomes? With model performance varying by application, training data often limited, and automated structure solution and docking still facing significant challenges, integrating AI effectively requires rigorous validation and close collaboration with experimental structural biology. Join computational scientists, structural biologists, and AI developers as they discuss how to translate AI advances into measurable SBDD improvements by:
- Identifying where AI delivers genuine value across SBDD, evaluating applications beyond protein folding prediction, including structure prediction, cryo-EM processing, molecular design, docking, and experimental data analysis
- Validating AI/ML approaches against real-world experimental data, discussing how independent benchmarking, wet-lab validation, and appropriate performance measures can establish confidence in new models and distinguish useful tools from overpromoted solutions
- Integrating AI with experimental structural biology, exploring how prediction models and experimental methods can complement one another to improve speed, fidelity, resolution, and decision making across the SBDD workflow
