Machine-learning robot could streamline drug development

Testing out newly developed drugs is an extremely time-consuming process, and it can be difficult to get right. Now, a team of scientists at Carnegie Mellon University (CMU) is working to streamline the task, creating a robotically-driven experimentation system that’s able to reduce the number of tests that have to be carried out by as much as 70 percent. When working on a new drug, scientists have to determine its effects to ensure that it’s both an effective treatment and not harmful to patients. This is hugely time-consuming, and it’s simply not practical to perform experiments for every possible set of biological conditions. That’s where CMU’s new robotic system steps in. It uses a machine learning approach to choose which experiments to conduct, using patterns in the data to accurately…

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