Willem Jespers

Assistant Professor in Computer-Aided Drug Discovery, University of Groningen |
 Founder & Chief Scientific Officer, MODSIM Pharma

Dr. Willem Jespers earned his MSc in Bio-Pharmaceutical Sciences from Leiden University in 2016, graduating cum laude. He completed his PhD at Uppsala University in 2020 under the supervision of Prof. Hugo Gutiérrez-de-Terán, focusing on computational methods for predicting ligand binding affinities. During his doctoral research, Dr. Jespers developed QligFEP, a computational tool based on Free Energy Perturbation (FEP) methodology, designed to accurately predict ligand binding affinities. This innovation led to the co-founding of MODSIM Pharma AB, a startup that commercializes QligFEP and other structure-based drug discovery approaches, where he serves as Chief Scientific Officer. After his PhD, Dr. Jespers worked at Galapagos NV, applying advanced computational techniques to guide ligand design projects in an industrial setting. He then held a position as Assistant Professor at Leiden University, where he combined his expertise in structure-based drug discovery with machine learning approaches developed in the Computational Drug Discovery group in Leiden. He became a tenured assistant professor at the Groningen Research Institute of Pharmacy (GRIP) in April 2025. In this role, where he leads a research group focusing on the integration of artificial intelligence (AI) and physics-based methods to advance early-stage drug discovery.

Presentation: From Mutated G Proteins to Molecular Interventions: Computational Strategies for Understanding and Targeting Cellular Signalling

G proteins play a central role in cellular signalling, and mutations in these molecular switches can strongly alter pathway activation, disease mechanisms and therapeutic response. In this talk, I will give a high-level overview of our work on computational approaches to understand and target mutated G proteins. I will discuss how molecular modelling, molecular dynamics, docking, free energy calculations and machine learning can be combined to investigate the structural and energetic consequences of G-protein mutations. These methods help reveal how mutations affect conformational dynamics, nucleotide binding, protein-protein interactions and ligandability. By linking atomistic simulations with drug discovery workflows, our research aims to identify opportunities for modulating aberrant G-protein signalling and to support the development of selective molecular interventions for challenging signalling targets.

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