Ed Moret

Associate Professor, UIPS

Dr. Ed E. Moret studied pharmacy in Utrecht and finished in 1988. In 1993 he finished his Ph.D. in Utrecht on a study of calculations and simulations of DNA-alkylating cytostatics. During a stay at the group from prof. Olson, Molecular Biology department of the Scripps Research Institute in La Jolla (San Diego, California, USA), he carried out research on the molecular recognition of antigens by antibodies with computer docking using the Autodock programme developed in this group.

In 1994 he was employed at the department of Medicinal Chemistry at Utrecht University as assistant professor. His expertise is in the field of computational medicinal chemistry, computer-aided drug discovery, cheminformatics and bioinformatics. Molecular recognition still is his primary interest, particularly in the field of immunology and he has supervised several Ph.D. and master students.

Ed Moret has taught courses for students of pharmacy, chemistry, UCU and (bio)medical sciences, as well as Ph.D. courses in bioinformatics and computer-aided drug discovery. He has his BKO- teaching qualification since 1998, his SKO- teaching qualification since 2008, and he participated in the “Centre of Excellence in University Teaching” class of 2005-2006.

In 2001 he switched to educational management. He developed and managed the master’s programme Drug Innovation, the profile Drug Regulatory Sciences as well as the Honours programme Pharmaceutical Sciences. In 2010 he switched to research management. He is managing director of the research institute UIPS, with five divisions, 50 PI’s and 180 PhD candidates.

Ed Moret was member of the editorial board of the journals Medicines and Conceptuur for Dutch medicine research and he was secretary of the Board of FIGON (2016-2025), secretary of the Raad voor de Farmaceutische Wetenschappen (2024-2025) and member of the Board of Stichting Farmaceutische Erfgoed (2024-).

Presentation: A gift to the world. Alphafold as a flywheel for democratic drug design.

Nobel prize winner (Chemistry, 2024) Demis Hassabis from Deepmind (Alphafold) said that if they can solve intelligence, they can solve everything else afterwards. He considered protein folding as a tough problem to tackle and treating disease as one of the most ambitious problems.

Progress in folding and docking was slow for decades, but deep learning models emerged and they became flywheels for new tools to tackle ambitious problems in life sciences. Many tools are free, democratising our research field of drug design. Our preliminary research findings with one of these tools, Boltz2, show how effective deep learning is compared to physics-based methods like Autodock and our research findings seem to suggest how this could help in more accurately predicting affinity, activity, selectivity, toxicity and resistance.

In the near future genAI will revolutionise Systems Pharmacology, so that we may finally understand who will actually benefit from an existing therapy or make new personalised therapies.

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