ICREA research professor and associate professor at Universitat Pompeu Fabra (UPF), group leader of the computational science laboratory. I have a background in applied mathematics (1997, University of Bologna) and a PhD in computational chemistry (2002, Queen Mary University of London). Previous work experiences include the CINECA supercomputing center (1998-1999), University College London (2003-2006) and Ramon y Cajal tenure-track fellowship at University Pompeu Fabra. The laboratory receives grants from national, international and leading industrial firms worldwide. I have published over 100 articles in international journals (PNAS, JACS, Nat. Chem., Nat. Commun., etc). I have been visiting professor at Stanford University in 2015 and IPAM-UCLA in 2019. My h-index is currently 36 with approximatively 840 citations per year as in 2019.
Prof. Gianni De Fabritiis leads the computational science laboratory whose interests are the application of computation to solve real world problems, where we define intelligence as a form of computation. The research group develops machine learning models with intelligent, useful behavior using reinforcement learning and deep learning, for specific environments. Biomedicine is one environment where physics-based simulations and machine learning to provide novel, innovative approaches. The group leads GPUGRID.net, one of the top distributed computing projects worldwide for running molecular simulations on GPUs and the open platform PlayMolecule.org that has around a thousand registered scientists. The group and its spin-off company Acellera have collaborated with major industries worldwide like Sony, Nvidia, HTC mobile, UCB, Pfizer, Biogen and Novartis.
– Skalic M, Sabbadin D, Sattarov B, Sciabola S & De Fabritiis G 2019, ‘From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design’, Molecular Pharmaceutics, 16, 10, 4282 – 4291.
– Jimenez-Luna J, Perez-Benito L, Martinez-Rosell G, Sciabola S, Torella R, Tresadern G & De Fabritiis G 2019, ‘DeltaDelta neural networks for lead optimization of small molecule potency’, Chemical Science, 10, 47, 10911 – 10918.
– Wang J, Olsson S, Wehmeyer C, Perez A, Charron NE, de Fabritiis G, Noe F & Clementi C 2019, ‘Machine Learning of Coarse-Grained Molecular Dynamics Force Fields’, Acs Central Science, 5, 5, 755 – 767.
– Galvelis R, Doerr S, Damas JM, Harvey MJ & De Fabritiis G 2019, ‘A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning’, Journal Of Chemical Information And Modeling, 59, 8, 3485 – 3493.
Selected research activities
I am CEO and CSO of the technology company Acellera Ltd, of which I am also founder.