Welcome to the Computational Science Laboratory
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 has collaborated with major industries worldwide like Sony, Nvidia, HTC mobile, UCB, Pfizer, etc.
Our new software HTMD is now available for download
High-throughput molecular dynamics is our concept for molecular simulation based discovery. The software implementation in Python is currently available for download at:
Research
Research Lines
Biomedicine. We use large distributed computational resources (GPUGRID.net) 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. We develop machine learning approaches applied to physics, chemistry and biology. We are particularly interested in reinforcement learning, Bayesian networks and deep learning.
Software
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 - 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.
Web resources
Playmolecule - Most of our methods, predictors and machine learning approaches are publicly available via this webGL enabled site.
Multiscale Laboratory GitHub - More tools are available in our github account. Follow us there.
HTMD github repository - HTMD main repository publicly accessible in github. Contribute!
The youtube channel of Acellera - Acellera posts a lot of material here
People
Current members of our research group
In our group we always value having the best people from different background and expertises
Current group
- Gianni De Fabritiis - Head of Computational Science laboratory at University Pompeu Fabra, Icrea research professor, Founder at Acellera
- Davide Sabbadin - Postdoc researcher at University Pompeu Fabra
- Dominik Lemm - Phd student at University Pompeu Fabra
- Boris Sattarov - Phd student at University Pompeu Fabra
- Jose Jimenez - Phd student at University Pompeu Fabra
- Miha Skalic - Phd student at University Pompeu Fabra
- Alejandro Varela - Phd student at University Pompeu Fabra
- Pablo Herrera Nieto - Phd student at University Pompeu Fabra
- Stefan Doerr - Research Engineer at Acellera
- Adria Perez - PhD student at University Pompeu Fabra
- Alberto Cuzzolin - Collaborator, staff scientist at Acellera
- Gerard Martinez - Collaborator, Staff at Acellera
- Joao M. Damas - Collaborator, staff scientist at Acellera
- Matt J. Harvey - Collaborator and HPC analyst at Imperial College London
Apply
Apply for a position
We are always looking for talented people who would like to join our laboratory. We praise on diversity of expertises, e.g. mathematics, chemistry, computer science, statistics, physics, biology, biotechnology, etc. If you are a passionate, hard working, self-motivated person with the ambition to do great science, send us an email at gianni.defabritiis@upf.edu.
OPEN POSITIONS
- PhD position - PhD in deep learning in computational structural biology
- General- If you are interested in doing a specific project idea with us send a CV and motivation letter to gianni.defabritiis@upf.edu
CLOSED POSITIONS
These positions are closed but we might have similar ones in the future.
- PhD position - PhD in artificial neural networks in structural bioinformatics
- PhD position - PhD in protein-protein recognition and design
- PhD position - PhD in intrinsically disordered proteins