John Chodera
Dr. Chodera joined the lab in 2006 after completing his PhD at UCSF in Ken Dill's lab. John plays a role in many projects in Folding@home, but I'll highlight two primary projects today.
First, with former FAH team member Nina Singhal (now a professor at University of Chicago), John has further developed a new way to use distributed computing to simulate long timescale dynamics. This is at the very heart of how FAH works, so this is a very exciting advance for us. The basic idea is to build a kinetic model of the long timescale process by diving the system into a series of states and calculating the transition probability between these states via molecular dynamics. To make all of this possible requires some nifty statistics (especially a fair amount of Bayesian statistics, which is becoming more and more important to how we do our work). The upshot is that we should now be able use FAH to do calculations which we couldn't do before, which is very exciting for us and hopefully will have a big impact on the broader scientific community as well.
Second, John has been working to further develop methods to calculate free energies, especially those relevant for biomolecules such as protein-ligand free energies. These types of calculations are particularly relevant for our plans for late stages drug design, but also represent a major advance in computational methodology in general.
Copied from Meet FAH team member: John Chodera (Vijay Pande's blog)
John Chodera
First, with former FAH team member Nina Singhal (now a professor at University of Chicago), John has further developed a new way to use distributed computing to simulate long timescale dynamics. This is at the very heart of how FAH works, so this is a very exciting advance for us. The basic idea is to build a kinetic model of the long timescale process by diving the system into a series of states and calculating the transition probability between these states via molecular dynamics. To make all of this possible requires some nifty statistics (especially a fair amount of Bayesian statistics, which is becoming more and more important to how we do our work). The upshot is that we should now be able use FAH to do calculations which we couldn't do before, which is very exciting for us and hopefully will have a big impact on the broader scientific community as well.
Second, John has been working to further develop methods to calculate free energies, especially those relevant for biomolecules such as protein-ligand free energies. These types of calculations are particularly relevant for our plans for late stages drug design, but also represent a major advance in computational methodology in general.
Copied from Meet FAH team member: John Chodera (Vijay Pande's blog)
Written by: jmn, On: 09/09/09