Computational Biomedicine
COMPUTATIONAL BIOMEDICINE
LEAD PI (S): DRS. PAOLO CARLONI AND MICHELE PARRINELLO
MENTOR: DR. ANDREA RIZZI
WEBSITE: https://www.fz-juelich.de/ias/ias-5/EN/Home/home_node.html
The project focuses on a new, machine-learning-based method that enables the prediction of molecular structures, free energies of binding, and free energy barriers with DFT-QM/MM accuracy from data collected with classical force field simulations. A preprint of the publication describing the new method and the first application will soon be available online. In the collaborative project, the student will apply, benchmark, and refine the methodology to the case of a neurobiologically-relevant receptor-ligand system.
The project is part of a larger initiative on “Innovative high-performance computing approaches for molecular neuromedicine” funded by the Helmholtz European Partnering program (PIs Paolo Carloni and Michele Parrinello). We anticipate that the work will lead to a publication in a high-impact journal. Furthermore, the project offers plenty of directions that can be explored to refine and improve the current methodology. Opportunities for follow-ups and longer-term collaborations between the groups will thus be abundant.
REQUIRED SKILLS
Students applying to this project are expected to have:
- previous experience with molecular simulations and
- basic knowledge of programming language (Python is preferred)
- previous experience in the following areas will be very helpful:
- Free energy calculations,
- QM/MM models,
- neural networks,
- HPC computing
COMPUTATIONAL RESOURCES
During the internship, the student will have the possibility to access the computational resources at Jülich Forschungszentrum and RWTH Aachen University IT Center, which include CPU and GPU cluster computing systems among the fastest and most energy-efficient in the world.