Computational Science Laboratory

Computation applied to Science

Our research interests are rooted in the application of computation to solve real world problems, based on the view that computation and intelligence are very much the same thing. Specifically, we develop new methods, programs and algorithms that we apply to areas such as AI, drug design, and protein folding. The group and its spin-off company Acellera has collaborated with major industries worldwide like Sony, Nvidia, HTC mobile, UCB, Pfizer, Biogen, Novartis, etc.

Highlights

PlayMolecule is a platform for drug discovery and structural biology applications based on physics-based simulations and deep learning. Try it out here

Unity Obstacle Tower RL challenge. We classified second at the Unity obstacle tower challenge, where an agent had to navigate a complex 3D environment and solve tasks. Here is the agent in action: Video, and the Unity blog post here

LogP SAMPL 2019 challenge. We scored second to the logP small molecule blind prediction challenge

D3R 2018 challenge. We won two sub-challenges in the D3R 2018 drug discovery blind challenge in collaboration with Acellera related to binding affinity and pose prediction of protein-ligand complexes

High-throughput molecular dynamics

Molecular dynamics engine on GPUs

Distributed computing

Industrial spin-offs

Community access

Scientific publications

Research

Biomedicine

We use computations, as physics-based simulations and machine learning to provide novel, innovative approaches in biomedicine

Machine Intelligence

We develop machine learning models with the aim to attain in specific environments intelligent, useful behavior using reinforcement learning and deep learning

Software

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/OpenMM has pioneered the use of GPUs for molecular simulations allowing for high-throughput simulations and ultimately leading to HTMD. ACEMD is still one of fastest molecular dynamics code and compatible with input files from Charmm and Amber. ACEMD is now based on OpenMM.

PyTorchRL is a scalable and modular reinforcement learning framework in PyTorch. Use it to write new algorithms and scale them up for real performance. This code currently hold the SOTA on Obstacle tower challenge by Unity3D.

Web Resources

Playmolecule is an application-based site that contains methods, predictors and machine learning approaches, most of which are publically available for drug discovery. Currently managed by Acellera.com.

Computational Science Laboratory GitHub contains more tools. Follow us there.

GPUGRID.net is one of the largest distributed computing project worldwide. GPUGRID pioneered the use of GPUs in distributed computing.

Apply

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 in tackling abstraction and reasoning with AI

PhD and Posdoctoral position in advancing reinforcement learning

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 in artificial neural networks in structural bioinformatics

PhD position in protein-protein recognition and design

PhD position in intrinsically disordered proteins

Contact

Tweets by gdefabritiis PRBB

Gianni De Fabritiis, GRIB, Barcelona Biomedical Research Park (PRBB), C/ Dr. Aiguader 87, 08003, Barcelona, Spain.
gianni.defabritiis at upf.edu