Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Robotically Driven System Could Reduce Cost of Discovering Drugs

Reporter: Irina Robu, PhD

However, their approach had only been tested using synthetic or previously acquired data, the team’s current model builds on this by letting the computer choose which experiments to do. The experiments were then carried out using liquid-handling robots and an automated microscope.

A total of 9,216 experiments were done, each consisting of acquiring images for a given cell clone in the presence of a given drug. The challenge for the algorithm was to learn how proteins were affected in each of these experiments, without performing all of them.

The originality of this work was to identify new phenotypes on its own as part of the learning process. To do this, it clustered the images to form phenotypes. The phenotypes were used to form a predictive model, so the learner could estimate the outcomes of unmeasured experiments. The basis of the model was to identify different sets of proteins that responded similarly to sets of drugs, so that it could predict the trend in the unmeasured experiments. The learner repeated the process for a total of 30 rounds, completing 2,697 out of the 9,216 possible experiments. As it progressively performed the experiments, it identified more phenotypes and more patterns in how sets of proteins were affected by sets of drugs.

Using an assortment of calculations, the team determined that the algorithm was able to learn a 92% accurate model for how the 96 drugs affected the 96 proteins, from only 29% of the experiments conducted.