Drug companies could save millions on research by employing a mathematical strategy akin to the one Netflix uses to suggest movies you’ll love.
Scientists have developed an algorithm that ranks chemical compounds based on their potential drug activity. The new technique, reported online in the Journal of Chemical Information and Modeling, outperforms current computational methods that seek therapeutic needles in enormous chemical haystacks.
The cost of developing a new drug is estimated by industry to be more than $1 billion. Much of this expense comes from the cost of pursuing initially promising molecules that ultimately fail, notes study coauthor Shivani Agarwal of MIT’s Computer Science and Artificial Intelligence Laboratory. Of 10,000 tested compounds, only one or two will make it to market.
Many researchers are turning to computers for help sorting through the millions of molecules in chemical libraries. Machine-learning techniques, which train computers with known solutions to a problem so it can then seek novel solutions on its own, can help researchers focus on the small number of really promising molecules, says Cynthia Rudin, an MIT expert in machine learning who was not involved with the research.