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.

From building the Deep Blue computer that beat Garry Kasparov at chess to the Watson artificial intelligence (A.I.) that won Jeopardy, IBM has been responsible for some high-profile public demonstrations of A.I. in action. Its latest showcase is less high concept, but potentially far more transformative -- applying machine learning technology to the subject of organic chemistry. As described in a new research paper, the A.I. chemist is able to predict chemical reactions in a way that could be incredibly important for fields like drug discovery. To do this, it uses a highly detailed data set of knowledge on 395,496 different reactions taken from thousands of research papers published over the years. Teo Laino, one of the researchers on the project from IBM Research in Zurich, told Digital Trends that it is a great example of how A.I. can draw upon large quantities of knowledge that would be astonishingly difficult for a human to master -- particularly when it needs to be updated all the time.