August 11, 2026 - by CSCS
Name
Francesco Luigi Gervasio
Position
Full Professor in Pharmaceutical Sciences, University of Geneva; Full Professor at University College London; Group Leader at the Swiss Institute of Bioinformatics.
Area of research
Computational chemistry, biophysics, bioinformatics, structural biology, and drug discovery.
My focus
My research focuses on developing new methods and using powerful computer simulations to understand the complex mechanisms of vital drug targets, with the ultimate goal of designing safer and more effective medicines. One major area of my work looks at kinases, specifically the MKK6-p38 complex that acts as a cellular switch for inflammation and stress responses. By combining simulations with experimental data, we aim to capture the fast and highly transient “dance” of these proteins as they associate, which could reveal new ways to block their interaction and overcome the limitations of current inhibitors. To further aid drug discovery, my group developed a new computational strategy called SWISH-X, which is designed to reliably detect elusive “cryptic pockets”—hidden cavities on the protein surface that are invisible in their normal state but pop open when a specific molecule binds to them. By accelerating the simulation of these slow, complex structural changes, SWISH-X allows us to uncover hidden binding sites at protein-protein interfaces, offering exciting new opportunities to develop drugs for targets that were previously considered “undruggable”.
What supercomputing and Alps mean for me
Alps is a crucial resource for my group. Without it we would not be able to perform our large-scale simulations.
What challenges do I face
One major challenge stems from our own success in algorithm development. The enhanced sampling methods we have developed—such as SWISH-X and OneOPES—are now very effective at navigating complex protein free-energy landscapes and accelerating slow conformational changes. They can therefore sample highly transient biological events like the formation of weak protein-protein complexes. Because of this, the computational bottleneck has shifted: The limits of current atomistic force fields that dictate how atoms interact are becoming increasingly evident. The force fields are approximate and imprecise, therefore the resulting simulations are not always reliable, which limits their usefulness for discovering and developing new drugs. At the same time, the rapid emergence of new ML methods presents both an exciting opportunity and a profound challenge. While these AI tools are increasingly used to predict protein structures and dynamics, they do not always extrapolate well, particularly for highly dynamic systems, novel target states, or cryptic pockets that lack extensive experimental training data. Consequently, a major open question in our field is how to best evaluate their true usefulness and how to integrate these data-driven ML predictions with rigorous, physics-based molecular simulations. Finding the right synergy between AI and physical models—without inheriting the biases of sparse training data—is crucial for the future of computational drug discovery.
What I like most about my work
What I find most thrilling about my research is the opportunity to use advanced molecular simulations and supercomputers as a powerful “virtual microscope”. In biology, many crucial events, such as the highly transient “dance” of two kinases coming together to pass on a cellular signal, happen too quickly or are too short-lived to be fully captured by traditional experimental techniques alone. I love being able to marvel at the complexity that emerges in these molecular systems and watch the beautiful dance of proteins unfold on my computer screen. Witnessing the invisible mechanics of life in unprecedented detail is deeply rewarding, as is the knowledge that deciphering these precise molecular choreographies and hidden structural vulnerabilities can ultimately lead to the design of much safer and more effective medicines.
My favourite recent project at CSCS
My favourite recent project explores the intricate “dance” between two crucial kinases, MKK6 and p38α, which act as molecular switches for cellular inflammation. Their interaction is so fast and transient that it is incredibly difficult to capture. Using extensive molecular dynamics simulations run on CSCS combined with cryo-electron microscopy, we finally revealed their surprising face-to-face conformation right before they activate. This breakthrough, recently published in Science, reveals how these vital signals propagate and opens exciting new paths to design better anti-inflammatory drugs.
Career background
Francesco Luigi Gervasio is currently a Full Professor of Pharmaceutical Sciences at the University of Geneva, Full Professor of Chemistry at Univeristy College London and Group Leader at the Swiss Institute of Bioinformatics. He earned his PhD in Chemistry from the University of Florence in 2001. His academic career includes positions as a postdoc and Oberassistent at ETH Zurich, a biophysics group leader at the Spanish National Cancer Research Center in Madrid, and a Full Professor of Chemistry and of Structural and Molecular Biology at University College London. His highly interdisciplinary research spans computational chemistry, biophysics, bioinformatics, structural biology, and drug discovery. He has made significant methodological contributions to the field, including the development of widely used enhanced sampling algorithms like SWISH and SWISH-X. He has an h-index of 59, over 15,000 citations, and more than 150 peer-reviewed publications in prestigious journals such as Science, Nature, and Cancer Cell.