Gianni De Fabritiis

Gianni De Fabritiis

Universitat Pompeu Fabra

Experimental Sciences & Mathematics

ICREA research professor, associate professor, and group leader of the Computational Science Laboratory at UPF, and CEO at Acellera Therapeutics. Bachelor's degree with honors in applied mathematics (1997) from the University of Bologna and a Ph.D. from the University of London (2002). I worked for the CINECA supercomputing center in Italy (1998-1999) and was a postdoctoral researcher at University College London (2003-2006). I founded Acellera Ltd and later Acellera Therapeutics Inc where I currently act as CEO. In 2008, I won a tenure-track Ramon y Cajal research position and later the national I3-tenured program. In 2014, I became an ICREA Research Professor. I performed research stays as a visiting professor at Stanford University and at UCLA. I have published over a hundred articles in high-ranking international journals with an h-index of 58 and over 15k citations, with 2500 citations per year in 2025. 

Research interests

The group's research interests are rooted in the applications of computation to science, where we regard intelligence as a form of computation itself. 
1) Molecular simulation and machine learning. We utilize computational methods, including physics-based simulations and modern machine learning, to develop novel and innovative methodological approaches in biomedicine.  
2) Computational intelligence. We investigate machine learning methods that would bring machine intelligence closer to human-level intelligence. We train intelligence agents using reinforcement learning in virtual environments. 

Selected publications

- Simeon G, Mirarchi A, Pelaez RP, Galvelis R & De Fabritiis G 2025, 'Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes', Journal of chemical theory and computation, 21 - 4 - 1831 - 1837.
- Zariquiey FS, Farr SE, Doerr S & De Fabritiis G 2025, 'QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials', Journal of chemical information and modeling, 65 - 8 - 4081 - 4089.

Thomas M, Bou A, Gomez-Tamayo JC, Tresadern G, Ahmad M, De Fabritiis G 2025, '- REINFORCE-ING Chemical Language Models for Drug Discovery', Journal of chemical information and modeling, 65 - 23 - 12752 - 12763.