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Computing Better Drugs

Threats to human health are a moving target. Viruses evolve. New syndromes emerge. Unfortunately, the timeframe for creating targeted drugs is measured in decades, not days.

Computer prediction of a novel inhibitor binding to
 the docking site of a protein target

Computer prediction of a novel inhibitor binding to
 the docking site of a protein target (JNK). The protein is involved in many diseases, including cancer.

Pengyu Ren, assistant professor of biomedical engineering at the university, is among a growing chorus of experts who believe the methods used by the pharmaceutical industry to find new drugs are a failure.

The problem: unrealistic models necessitated by limited computing power.

"They're taking shortcuts, making approximations of physical models," said Ren.

"The promise of rapid, inexpensive computational drug discovery has thus far eluded scientists," Michael Gonzales, life sciences program director at TACC, said. "Pengyu's work is an excellent example of how current advances in computing power are enabling scientists to take a fundamentally different approach to virtual drug discovery."

Using the Ranger supercomputer, Ren and colleagues at the Texas Institute for Drug and Diagnostic Development embarked on an ambitious search for new ways to find useful molecules for medicine, a process called drug discovery. The work has focused on evaluating best practices and applying new methods to 200 proteins that have known drug compounds. Ren believes this methodological shoot-out will lead to a more effective approach to drug discovery that will be adopted by the pharmaceutical industry.

In the meantime, he is using Ranger to understand the relationship between the rigidity of a drug compound and its ability to bind to a target protein, and to search for inhibitors relevant to cancer and heart disease in collaboration with experimentalists at the university.

"The ultimate goal is to develop tools that guide drug discovery," said Ren. "If that works, it will significantly improve our ability to design drug candidates that are more potent with fewer side-effects."