The Folding@home software allows you to share your unused computer power - so that we can research even more potential cures. While you keep going with your everyday activities, your computer will be working to help us find cures for diseases like cancer, ALS, Parkinson's, Huntington's, Influenza and many others.
Find the version of the software you prefer and get started. Downloading Folding@home is completely free, easy to install and safe to use.
What does "Folding" mean?
Folding refers to the way human protein folds in the cells that make up your body. We rely on the proteins to keep us healthy and they assemble themselves by folding. But when they misfold, there can be serious consequences to a person's health.
About the project
Folding@home (FAH or F@h) is a distributed computing project for disease research that simulates protein folding, computational drug design, and other types of molecular dynamics. Folding@home was developed by the Pande Laboratory at Stanford University in 2000.
Its main purpose is to determine the mechanisms of protein folding, which is the process by which proteins reach their final three-dimensional structure, and to examine the causes of protein misfolding. This is of interest to medical research into Alzheimer's disease, Huntington's disease, and many forms of cancer, among other diseases.
The project has pioneered the use of graphics processing units (GPUs), PlayStation 3s, Message Passing Interface (used for computing on multi-core processors) for distributed computing and scientific research. The project uses statistical simulation methodology that is a paradigm shift from traditional computing methods. As part of the client-server model network architecture, the volunteered machines each receive pieces of a simulation (work units), complete them, and return them to the project's database servers, where the units are compiled into an overall simulation.
Folding@home is one of the world's fastest computing systems, with a speed of approximately 98.7petaFLOPS as of March 2020. This performance from its large-scale computing network has allowed researchers to run computationally costly atomic-level simulations of protein folding thousands of times longer than formerly achieved.
