Dr. Christine Allen, a professor at the Faculty of Pharmacy in Leslie Dan and post-doctoral researcher Pauric Bannigan recently published a review paper on the topic in the Controlled Release Journal. That they explained about the growing trend in the field is reducing uncertainty around drug discovery by using artificial intelligence (AI) as a prediction tools.
Researchers normally conduct a high number of experiments by testing hundreds of possibilities to find promising solutions. Nevertheless, the introduction of AI-based prediction tools will change the experimentation process significantly. As described by Bannigan, AI prediction tools have the potential to narrow the starting point from which researchers must start experimenting. AI can guide scientists towards potential avenues for success by eliminating incompatible solutions, thereby saving both time and money. Hypothetically, pharmaceutical scientists can only require 10 experiments driven by these devices to bring out accurate results instead of a hundred.
Allen’s research group has now begun to collaborate with U of T professor Alán Aspuru-Guzik, who has significant expertise in applying AI to chemistry, as there is a growing trend of Big Pharma companies partnering with those specializing in AI. The teams worked together to use algorithms to help predict which materials could best be used to discover drugs.
As for AI’s impact on researchers, Allen noted that as AI tools become more involved in industries, human judgment remains highly valued, and is one of Prediction Machine’s main ideas. While AI could guide researchers by providing predictions, Dr. Avi Goldfarb, a professor at the Rotman School of Management noted that once they have them, their human judgment would still be valuable in deciding what to do with the predictions.