Gianni De Fabritiis

Universitat Pompeu Fabra

Experimental Sciences & Mathematics

ICREA research professor at Universitat Pompeu Fabra (UPF) and group leader of the computational science laboratory and associate professor at UPF.

I have  a bachelor degree in applied mathematics (1997, University of Bologna) and a PhD in computational chemistry (2002, Queen Mary University of London). I have  worked at the CINECA supercomputing center  (1998-1999),  at University College London (2003-2006) and later I won a 5 year Ramon y Cajal tenure-track fellowship at University Pompeu Fabra and the I3 program, before becoming ICREA Professor.

I have published 80 articles in international journals (PNAS, JACS, Nat. Chem., Nat. Commun., etc). My h-index is 30 with over 3200 citations. In the last four years I gave 30 oral presentations at international conferences (18 as invited speaker) and lectures in many pharmaceutical companies. I am associate editor of “In-silico pharmacology”.

Research interests

My group research interests are centered in the application of computing as a fundamental methodology to problem solving. In particular simulations in biomedicine, machine learning of biological data and machine intelligence.

Research lines

Biomedicine. We use large distributed computational resources ( with thousands of GPUs for molecular dynamics simulations, binding prediction, binding kinetics, Markov state models, online sampling methods (ACEMD, HTMD). The approach is computational driven but we like to collaborate with experimental laboratories and pharmaceutical companies.

Machine Intelligence. In this research line we develop machine learning approaches applied to biological data. We are particularly interested in  behavioral intelligence, artificial neural networks, sparse coding, deep and hierarchical learning.


Selected publications

– De Mol E, Szulc E, Di Sanza C, Martínez-Cristóbal P, Bertoncini CW, Fenwick RB, Frigolé-Vivas Marta, Masín M, Hunter I, Buzón V, Brun-Heath I, García J, De Fabritiis G, Estébanez-Perpiñá E, McEwan IJ, Nebreda ASalvatella X 2018, ‘Regulation of Androgen Receptor Activity by Transient Interactions of Its Transactivation Domain with General Transcription Regulators‘, Structure, 26(1):145-152.e3.

– Ferruz N, Doerr S, Vanase-Frawley MA, Zou Y, Chen X, Marr ES, Nelson RT, Kormos BL, Wager TT, Hou X, Villalobos A, Sciabola S & De Fabritiis G 2018, ‘Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs’, Scientific Reports, 8, 897.

– Jimenez J, Skalic M, Martinez-Rosell G & De Fabritiis G 2018, ‘K-DEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks’, Journal Of Chemical Information And Modeling, 58, 2, 287 – 296.

– Martinez-Rosell G, Harvey MJ & De Fabritiis G 2018, ‘Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors’, Journal Of Chemical Information And Modeling, 58, 3, 683 – 691.

– Perez A, Martinez-Rosell G & De Fabritiis G 2018, ‘Simulations meet machine learning in structural biology’, Current Opinion In Structural Biology, 49, 139 – 144.

– PlayMolecule BindScope: Large scale CNN-based virtual screening on the web, M Skalic, G Martínez-Rosell, J Jiménez, G De Fabritiis. Bioinformatics, Ahead 30/1/2019