A neural network picks promising antibiotics out of a library of chemicals

Biochemists have had some success at designing drugs to match specific targets. But much of drug development remains an arduous grind, screening hundreds to thousands of chemicals for a "hit" that has the effect you're looking for. There have been several attempts to perform this grind in silico, using computers to analyze chemicals, but they've had mixed results. Now, a US-Canadian team is reporting that it's modified a neural network to handle chemistry and used it to identify a potential new antibiotic.

Artificial neurons meet chemicals

Two factors have a major influence on the success of neural networks: the structure of the network itself, and the training it undergoes. In this case, the training was pretty minimalist. The research team did the training using a group of 1,760 drugs that were previously approved by the US FDA, along with another 800 or so natural products. Most of these aren't antibiotics; they target a variety of conditions and are made up of largely unrelated molecules. The researchers simply tested whether these slowed down the growth of E. coli. Even though many of them were partially effective, the researchers set a cutoff and used that to provide a yes or no answer.

This approach does have some advantages in that it shouldn't bias the resulting neural network for any particular chemical structure. But with a dataset that small, it's likely that some specific functional chemical groups were left out of the training set entirely. Success was also very rare, with only 120 molecules coming in above the cutoff. And, since the cutoff was a binary "works" or "doesn't work," the network had no way of identifying trends that could help it project what chemicals might be more active.