The researchers started their work by feeding databases of known drug molecules into an AI model that would predict whether a compound would have a biological effect. Then, they used an XAI model developed by collaborator Pascal Friederich at Germany’s Karlsruhe Institute of Technology to examine the specific parts of the drug molecules that led to the model’s prediction. This helped explain why a particular molecule had activity or not, according to the model, and that helped Davis and Sturm understand what an AI model might deem important and how it creates categories once it has examined many different compounds.
The researchers realized that XAI can see things that humans might have missed; it can consider far more variables and data points at once than a human brain. For example, when screening a set of penicillin molecules, the XAI found something interesting. “Many chemists think of penicillin’s core as the …