Strategic Research

Computational Science group of GRIB

The Computational science research group led by Gianni de Fabritiis is dedicated to computational science in biomedicine and machine learning. The group research interests are rooted in application of computation to solve real world problems. Specifically, we develop new methods and algorithms and we apply them to computational chemistry, drug design, protein folding, etc. The group and the spin-off company Acellera, founded in 2006, has collaborated with major industries worldwide like Sony, Nvidia, HTC mobile, UCB, Pfizer, etc.

The current research lines of the group are:

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 industries where we work by rationalizing experimental results.

Machine Intelligence. In this new research line we develop machine learning approaches applied to the biological data. We are particularly interested in dimensionality reduction, artificial neural networks, unsupervised learning, reinforcement learning, sparce coding, deep and hierarchical learning.


Web Resources

The Computational Science group of GRIB is integrated in the Research Programme on Biomedical Informatics, GRIB. The GRIB is a joint research programme of the Hospital del Mar Medical Research Institute (IMIM) and the Department of Experimental and Health Sciences of the Universitat Pompeu Fabra. The GRIB mission is to develop and apply computational methods and information technologies for a better understanding and prediction of biological phenomena, giving especial emphasis to those related to the human diseases, their prevention, diagnosis and pharmacological treatment.

Main projects

  • 1.

    CompBioMed: A Centre of Excellence in Computational Biomedicine

    CompBioMed is a user-driven Centre of Excellence in Computational Biomedicine, to nurture and promote the uptake and exploitation of high performance computing within the biomedical modelling community. European project funded by the H2020 for period 2016-2019.

  • 2.

    Conformational kinetics, binding and modulation of disordered protein domains

    2015-2017 (BIO2014-53095-P) MINECO



Bioinformatics expertise:

Group Leader:

Gianni de Fabritiis



Chus Donlo

Bioinformatics services offered

  • HTMD

    HTMD is a Python platform for computational biology, including molecular simulations, docking, Markov state models, molecule manipulation, build tools for Amber and Charmm, visualization (webGL and VMD), adaptive sampling and more. Imagine setting up an entire computational experiment in a single, simple Python script.


    ACEMD has pioneered the use of GPUs for molecular simulations allowing for high-throughput simulations and ultimately leading to HTMD. ACEMD is still one of top molecular dynamics code, simple to use with a NAMD like syntax and compatible with input files from Charmm and Amber.