researchers at Carnegie Mellon have developed a robotic experimentation system to make the drug development process faster , cheaper and more efficient . The system involves " active learning " by a robot that repeatedly chooses which drug to carry out and learns from the patterns it observes in the data . The algorithm was able " to produce a 92 % accurate estimate for how the 96 drugs affected the 96 proteins " from only 29 % of the possible 10k experiments conducted , the researchers concluded .
Scientists at Carnegie Mellon have developed a robotic experimentation system to make the drug development process faster, cheaper and more efficient. The system involves "active learning" by a computer that repeatedly chooses which experiments to carry out and learns from the patterns it observes in the data. The algorithm was able "to learn a 92% accurate model for how the 96 drugs affected the 96 proteins" from only 29% of the possible 10k experiments conducted, the team said.