ICREA research professor, associate professor, and group leader of the Computational Science Laboratory at UPF, and CEO/CSO at Acellera Ltd. 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). In 2006, I founded Acellera Ltd where I currently act as a CSO. In 2008 I won a tenure-track Ramon y Cajal research position and later the national I3-tenured program. In 2014 I became ICREA Research Professor. I performed research stays as a visiting professor at Stanford University and at UCLA. He has published over a hundred articles in high-ranking international journals with an h-index of 50 and over 10000 citations with 1650 citations per year in 2023.
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 use computation such as physics-based simulations and modern machine learning to provide novel, 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, we built scalable software for reinforcement learning and low-sample learning.
Selected publications
- Majewski M, Perez A, Tholke P, et al. 2023, 'Machine learning coarse-grained potentials of protein thermodynamics', Nature Communications, 14, 1, 5739-5739.
- Eastman, P et al. 2023, 'OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials', Journal of physical chemistry b, 128 - 1 - 109 - 116.
- Cremer J, Sandonas LM, Tkatchenko A, Clevert DA, De Fabritiis G 2023, 'Equivariant Graph Neural Networks for Toxicity Prediction', Chemical research in toxicology, 36 - 10 - 1561 - 1573.
- Navarro C, Majewski M & De Fabritiis G, 2023 'Top-Down Machine Learning of Coarse-Grained Protein Force Fields', Journal of chemical theory and computation, 19 - 21 - 7518 - 7526.
- Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE, Chodera JD & De Fabritiis G 2023, 'NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics', Journal Of Chemical Information And Modeling, 63, 18, 5701-5708.
- Herrera-Nieto P, Perez A & De Fabritiis G 2023, 'Binding-and-Folding Recognition of an Intrinsically Disordered Protein Using Online Learning Molecular Dynamics', Journal Of Chemical Theory And Computation, 19, 13, 3817-3824.
- Sabanés Zariquiey F, Pérez A, Majewski M, Gallicchio E & De Fabritiis G 2023, 'Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series', Journal Of Chemical Information And Modeling, 63, 8, 2438 - 2444.
- Eastman P, Behara PK, Dotson DL et al. 2023, 'SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials', Sci Data 10, 11.