The Intel-United Devices Cancer Research Project will advance research to uncover new cancer drugs through the combination of chemistry, computers, specialized software, and organizations and individuals who are committed to fighting cancer.

The research centers on proteins that have been determined to be a possible target for cancer therapy. Through a process called "virtual screening", special analysis software will identify molecules that interact with these proteins, and will determine which of the molecular candidates has a high likelihood of being developed into a drug. The process is similar to finding the right key to open a special lock—by looking at millions upon millions of molecular keys.

Participants in the Intel-United Devices Cancer Research Project are sent a unit of molecules over the Internet. Their PC will analyze the molecules using drug-design software called LigandFit. The LigandFit software analyzes the molecular data by creating a three-dimensional model and changing its shape (or conformation) to attempt to dock it into a protein site. When a conformation docks successfully and triggers an interaction with the protein, it registers as a "hit". These hits are what this research hinges on. Any one hit may be the one that will ultimately lead to a cure. All hits are recorded, ranked as to strength, and filed for the next stage of the project. This project is anticipated to be the largest computational chemistry project ever undertaken. And the more individuals who volunteer their PCs, the more power available to move the project forward.

Over five decades of cancer research effort in drug discovery and development have yielded more than 40 drugs for the treatment of cancers. These anti-cancer drugs are extending the lives of many people with cancer, but often at great cost. The side effects commonly associated with cancer therapy often may seem as bad as the disease itself. Side effects can be so severe that they limit the dosages patients can receive. And half of all cancer patients fail to respond to the therapies currently available.

Despite these shortcomings, these therapies are still quite costly—a considerable amount of money is spent on treating cancer. Treatment of cancers account for over 6% of all health care costs. The National Institutes of Health estimate cancer is responsible for $37 billion for direct medical costs, and $11 billion in lost productivity due to illness. The discovery of new drugs represents the best hope to fight both the rising medical costs and the suffering associated with current cancer therapies.

There could hardly be a scourge more worth fighting—the high mortality rate, the suffering experienced by patients, and the high costs of treatment make fighting this disease a research priority.

Time is an important factor to making a cure available, especially with a disease as deadly as cancer. The road from idea, to discovery, to testing, to going to market can be a long process, but every day that is shaved off the time to drug availability may save hundreds of lives.

Even with extensive pre-screening, the whittled-down number of molecules to review for this project is estimated at over two hundred million for each protein—a daunting number. Analyzing this quantity of anything requires an enormous amount of computational power. And when the numbers are this big, even supercomputing is limited. A super computer has a peak capacity. That is, if a workload is three times the capacity of the computer, the jobs must be "queued up" and attacked consecutively. A project like this one might take so much time that a researcher wouldn't even embark on it—he or she wouldn't see the end result in their lifetime. However, with distributed computing, thousands or even millions of individual computers can each work on different molecules simultaneously, and the time to results can be significantly lessened.

Internet distributed computing allows research of amazing scope and complexity. If this project is successful, the public contribution of computing power could become one of the most powerful tools available to modern drug research.