Researchers from Carnegie Mellon University (CMU) have created the first robotically driven experimentation system to determine the effects of a large number of drugs on many proteins, reducing the number of necessary experiments by 70%.
The model, presented in the journal eLife, uses an approach that could lead to accurate predictions of the interactions between novel drugs and their targets, helping reduce the cost of drug discovery.
“Biomedical scientists have invested a lot of effort in making it easier to perform numerous experiments quickly and cheaply,” says lead author Armaghan Naik, a Lane Fellow in CMU’s Computational Biology Department.
“However, we simply cannot perform an experiment for every possible combination of biological conditions, such as genetic mutation and cell type. Researchers have therefore had to choose a few conditions or targets to test exhaustively, or pick experiments themselves. The question is which experiments do you pick?”
Naik says that careful balance between performing experiments that can be predicted confidently and those that cannot is a challenge for humans, as it requires reasoning about an enormous amount of hypothetical outcomes at the same time.
To address this problem, the research team has previously described the application of a machine learning approach called “active learning”. This involves a computer repeatedly choosing which experiments to do, in order to learn efficiently from the patterns it observes in the data. The team is led by senior author Robert F. Murphy, Professor at the Ray and Stephanie Lane Center for Computational Biology, and Head of CMU’s Computational Biology Department.