CAMBRIDGE, Mass. — Ranking algorithms are one of the hottest topics in computer science: they’re what determines the order of Google’s search results and which movies and books Netflix and Amazon recommend to their customers. Now researchers at MIT and Harvard Medical School have shown that ranking algorithms could find an important application in a somewhat surprising field: drug development.
Drug development typically begins with the identification of a “target” — a molecule involved in the biological processes underlying some disease. The next step is to try to find chemicals that either promote or suppress the molecule’s production. Scientists have assembled huge libraries — both virtual and physical — of chemical compounds that might be active against biological targets, and drug developers who have identified a target usually select a group of candidate drugs from those libraries.
But the majority of drug candidates fail — they prove to be either toxic or ineffective — in clinical trials, sometimes after hundreds of millions of dollars have been spent on them. (For every new drug that gets approved by the U.S. Food and Drug Administration, pharmaceutical companies have spent about $1 billion on research and development.) So selecting a good group of candidates at the outset is critical.
Drug companies have been using artificial-intelligence algorithms to help select drug candidates since the late 1990s. But Shivani Agarwal, a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory, Deepak Dugar, a graduate student in chemical engineering, and the Harvard Medical School’s Shiladitya Sengupta have shown that even a rudimentary ranking algorithm can predict drugs’ success more reliably than the algorithms currently in use.
The improvements were relatively modest, but to Agarwal, they’re an indication that recent research on more sophisticated ranking algorithms holds real promise for drug discovery. “The algorithms we use are actually very basic,” she says. “So there’s a lot of scope for even further improvement using more-optimized ranking algorithms.”
How they did it: At a general level, the new algorithm and its predecessors work in the same way. First, they’re fed data about successful and unsuccessful drug candidates. Then they try out a large variety of mathematical functions, each of which produces a numerical score for each drug candidate. Finally, they select the function whose scores most accurately predict the candidates’ actual success and failure.
The difference lies in how the algorithms measure accuracy of prediction. When older algorithms evaluate functions, they look at each score separately and ask whether it reflects the drug candidate’s success or failure. The MIT researchers’ algorithm, however, looks at scores in pairs, and asks whether the function got their order right.
Next steps: Agarwal is investigating algorithms that maximize the accuracy of the rankings at the top of a list, even at the expense of lower rankings, since drug developers are generally interested in only a handful of the most promising drug candidates.
Source: “Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach,” Journal of Chemical Information and Modeling, Apr. 13, 2010
Funding: National Science Foundation, Department of Defense Era of Hope Scholar Award, Mary Kay Ash Charitable Foundation
contact: Jessica Holmes – MIT News Office
written by: Larry Hardesty, MIT News Office