We build at the interface between computer simulations and computational intelligence
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Over the years, we have pioneered several research areas:
Neural network potentials (TorchMD-NET).
Differentiable simulations (TorchMD).
Won several blind challenges in drug discovery and second place in the Unity Obstacle challenge using reinforcement learning.
First and fastest MD on GPUs with ACEMD and co-principal investigator in OpenMM.
First distributed computing projects initially on SONY PlayStation3 and later on GPUs (GPUGRID.net).
Generative models on molecular structures (LigDream, LigaNN, etc).
Machine learning on three-dimensional molecular structures (Kdeep, DeepSite, etc).
High-throughput molecular simulations (HTMD) software.
Bitcoin donations for Science with the project Donate@home.
Research
Research
Computer Simulations
Computer Simulations
The fundamental equations that govern Nature at atomistic scales are well understood in terms of quantum mechanics. Solving such equations is not possible apart from very simple systems, yet solutions to this problem represent one of the grand challenges for computational sciences as it would allow an understanding of all properties of molecular systems. We investigate this challenge by solving the sampling and accuracy problems of atomistic simulations using machine learning, physics, and GPUs.
Machine Learning
Machine Learning
Understanding and replicating human-level intelligence in machines is of great importance for the progress of humanity. We look at intelligence as a physics problem trying to understand the key components. Two key factors in human intelligence are prediction and population learning. Recently, we ranked second in the Unity obstacle tower challenge, an AI testbed for navigation, puzzle solving, etc. We currently own the SOTA on this challenge using PytorchRL.
Projects
Projects
Distributed Computing Simulations for Science
Distributed Computing Simulations for Science
GPUGRID.net is one of the largest distributed computing project worldwide. GPUGRID pioneered the use of GPUs in distributed computing.
The first biomolecular simulation on GPUs
The first biomolecular simulation on GPUs
ACEMD has pioneered the use of GPUs for molecular simulations allowing for high-throughput simulations. ACEMD is still one of fastest molecular dynamics code and compatible with input files from Charmm and Amber.
An AI co-scientist for drug discovery.
An AI co-scientist for drug discovery.
Playmolecule is an application-based site that contains methods, predictors and machine-learning approaches, most of which are publicly available for drug discovery—currently managed by Acellera.
Molecular simulations on GPUs ACEMD/OPENMM
Molecular simulations on GPUs ACEMD/OPENMM
OpenMM is a high-performance toolkit for molecular simulation. Use it as an application, a library, or a flexible programming environment. We co-lead OpenMM together with John Chodera from MSKCC, Tom Markland and Peter Eastman from U. Stanford.
A framework for machine learning molecular simulations
A framework for machine learning molecular simulations
TorchMD intends to provide a simple to use API for performing molecular dynamics using PyTorch. This enables researchers to more rapidly do research in force-field development as well as integrate seamlessly neural network potentials into the dynamics, with the simplicity and power of PyTorch.
Unity Obstacle Tower Challenge
Unity Obstacle Tower Challenge
We participated to this Neurips competition organized by Unity for autonomously solving a puzzle game using reinforcement learning. We got the second position agent.
TorchRL is a pytorch-based library for RL that allows to easily assemble RL agents using a set of core reusable and easily extendable sub-modules as building blocks.
GitHub: https://github.com/pytorch/rl
HTMD is a Python platform for computational biology, including molecular simulations, docking, Markov state models, molecule manipulation, build tools, adaptive sampling. Entire experiments can be coded in a single Python script.
Positions
Positions
Apply
Apply
We are always looking for talented people. We praise on diversity of expertise, e.g., mathematics, chemistry, computer science, statistics, physics, etc. If you are a hard-working, self-motivated person with the ambition to do great science, send us an email at gianni.defabritiis@upf.edu.
Publications
Publications
Spin-offs
Spin-offs
Developing the algorithms to automate drug discovery.
Contacts
Contacts
Gianni De Fabritiis, Icrea research professor at University Pompeu Fabra, Head of Computational Science Laboratory, PRBB, Barcelona.
Email: gianni.defabritiis at upf.edu
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