Michael Deem, 30
University of California, Los Angeles
Combinatorial chemistry is a radical departure from the way researchers have traditionally identified new drugs and materials. Rather than painstakingly making and testing compounds one at a time, in combinatorial chemistry you make hundreds of thousands--even millions--of variations at the same time, then screen to find the winners. Michael Deem is working to improve the odds in this high-tech game of chance. By making an analogy between a computer simulation technique called Monte Carlo and combinatorial chemistry, Deem has provided a way to search more efficiently and broadly for new compounds. In general, Monte Carlo simulations are a powerful technique to sample data, using an algorithm that takes random "walks" among large data sets. Taking advantage of his chemical engineering background, Deem has developed "biased" Monte Carlo techniques that allow combinatorial chemists to greatly expand their searches, with the computer selecting the most promising paths.
Among other research projects, Deem is helping advance a new field called protein molecular evolution. "It’s basically combinatorial chemistry for proteins," says Deem. The payoff could be a powerful new technique for finding protein-based therapeutics.