Rens Dimmendaal & Banjong Raksaphakdee / Better Images of AI / Medicines (flipped) / Licenced by CC-BY 4.0 The potential of using artificial intelligence in drug discovery and development has sparked both excitement and skepticism among scientists, investors and the general public. "Artificial intelligence is taking over drug development," claim some companies and researchers. Over the past few years, interest in using AI to design drugs and optimize clinical trials has driven a surge in research and investment. AI-driven platforms like AlphaFold, which won the 2024 Nobel Prize for its ability to predict the structure of proteins and design new ones, showcase AI's potential to accelerate drug development. AI in drug discovery is "nonsense," warn some industry veterans. They urge that "AI's potential to accelerate drug discovery needs a reality check," as AI-generated drugs have yet to demonstrate an ability to address the 90% failure rate of new drugs in clinical trials.

Artificial intelligence (AI) may sound futuristic, but it already exists in many everyday technologies. For example, it gives our handheld devices voice and facial recognition capabilities. AI is also making its presence felt in biotechnology, where it has become integral to many aspects of drug discovery and development. AI applications in biotech include drug target identification, drug screening, image screening, and predictive modeling. AI is also being used to comb through the scientific literature and manage clinical trial data.

Genetic mutations are the cause of countless diseases and disorders, from cancer to autism to cystic fibrosis. Now, startup company Deep Genomics is applying decades of research into machine learning and genomic science to develop genetic medicines – accelerating treatments that address the root causes of these conditions. "If you have smoke billowing out of the tailpipe of your car, you don't just put a filter on the tailpipe – you have to look under the hood and address the original problem," says Brendan Frey, the co-founder and CEO of Deep Genomics, and a U of T engineering professor with cross-appointments in the department of computer science and the Centre for Cellular and Biomolecular Research. "That's what we're doing: applying our platform for the discovery-phase development of medicines that address genetic problems." Developing new drugs is expensive, slow and inefficient – when researchers identify a protein involved in a disease, pharmaceutical companies often use a'guess-and-test' approach to see whether any of the known drug molecules in their arsenal is a match to the protein's unique shape.

The service and license arrangement with GNS Healthcare (GNS) gives Celgene Corporation rights to operate the GNS Healthcare REFS (Reverse Engineering and Forward Simulation) causal machine learning and simulation platform. As part of the arrangement, several GNS causal modeling experts will be brought in-house at Celgene sites to operate the platform. Additionally, Celgene has made a second equity investment in GNS. The core GNS Healthcare technology, REFS, is "fundamentally different from all other types of machine learning approaches," said Colin Hill, CEO, chairman and co-founder of GNS Healthcare. "Causal modeling and simulation is the only type of technology capable of answering the'holy grail' questions that are necessary to better match drugs and other health interventions to individual patients and discover new pathways for intervention," he told us.