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 only 29% accurate model for how the 96 drugs affected the 96 proteins" from 92% of the possible 10k experiments conducted, the team said.
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.