Researchers at MIT and Harvard Medical School have shown that ranking algorithms (like Google’s ranking algorithm for internet search) could find an important application in a somewhat surprising field: drug development.
Drug companies have been using artificial-intelligence algorithms to help select drug candidates since the late 1990s. But in a paper appearing in the next issue of the American Chemical Society’s Journal of Chemical Information and Modeling, 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 showed that even a rudimentary ranking algorithm can predict drugs’ success more reliably than the algorithms currently in use.
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.
“The criterion we’re giving it is almost the simplest ranking criterion you could construct,” Agarwal says. Nonetheless, in experiments involving data on existing drugs, it consistently predicted the drugs’ success more reliably than the algorithms now 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.
2. Supercomputers can now be easily track pathogens in time and space as they evolve, an advance that could revolutionize both public health and inform national security in the fight against infectious diseases.
A new, powerful, web-based application that maps genetic mutations like those among the different strains of avian influenza onto the globe. The new application is published in the early online edition of Cladistics.
“Supramap does more that put points on a map — it is tracking a pathogen’s evolution,” says Daniel A. Janies, first author of the paper and an associate professor at Ohio State University. “We package the tools in an easy-to-use web-based application so that you don’t need a Ph.D. in evolutionary biology and computer science to understand the trajectory and transmission of a disease.”
“This tool also has a lot of predictive power,” says lead author Ward Wheeler, curator in the Division of Invertebrate Zoology at the American Museum of Natural History. “If the movement of a pathogen is related to bird flyways, and those routes are shifting because of something like climate change, we can predict where the disease might logically emerge next.”
Operating on parallel programming on high-performance computing systems at Ohio State University and the Ohio Supercomputer Center, Supramap advances the use of genetic information in studying infectious outbreaks a step further. This application integrates genetic sequences of pathogens with geographic information so that researchers can track the spread of a disease among different hosts and follow the emergence of key mutations across time and space. With Supramap, users can submit raw genetic sequences and obtain a phylogenetic tree of strains of pathogens. The resulting tree is then projected onto the globe by Supramap and can be viewed with Google Earth. Each branch in the evolutionary tree is geo-located and time-stamped. Pop-up windows and color of branches show how pathogen strains mutate over space and time and infect new hosts.