Pharmaceutical companies spend a lot of time testing potential drugs, and they end up wasting much of that effort on candidates that don't pan out. Kyle Swanson wants to change that. A master's student in computer science and engineering, Swanson is working on a project that involves feeding a computer information about chemical compounds that have or have not worked as drugs in the past. From this input, the machine "learns" to predict which kinds of new compounds have the most promise as drug candidates, potentially saving money and time otherwise spent on testing. Several prominent companies have already adopted the software as their new model.

San Francisco,Nov30:Riva-Melissa Tez was searching online for research that might help her father. He'd gone into a coma after suffering a stroke, and she wondered what the latest recommendations said--whether playing music to him in his native language could keep him connected to this world, or if giving him Prozac could boost his chances of recovery as it had done for mice in a study last year. A company named Iris launched a first version of that type of assistant. The machine can currently read the abstract of a paper, map out its key concepts, and find papers relevant to those concepts. It provides a quick way to get a sense of the scientific landscape for a given topic, something especially useful when you don't know the exact keywords for the type of research you are looking for.