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 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.


  • 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.

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