Just ahead of the J.P. Morgan Healthcare conference where we learned that artificial intelligence is becoming the status quo rather than a revolutionary "big idea" for new companies bringing breakthrough treatments to market, University of Toronto published research confirming that AI holds far more potential in R&D than is being taken advantage of.
The study, published in Nature Communications, tests the use of machine learning models in the development of long-acting injectable drugs. The findings support the hypothesis VeriSIM Life bases its entire mission around: that “AI is transforming the way we do science,” as worded by Alán Aspuru-Guzik, professor in chemistry and computer science at the university.
“This is a perfect example of a ‘Before AI’ and an ‘After AI’ moment and shows how drug delivery can be impacted by this multidisciplinary research,” he said in regards to the results of the study. Indeed, scientists at University of Toronto found that integrating artificial intelligence into the drug development process sped it up significantly, demonstrating the potential of this type of technology for getting life-saving treatments to patients faster.
This discussion around budding research into the benefits of AI and machine learning in drug discovery matters not only because it demonstrates that these topics aren’t going away anytime soon, but that it further validates the ability of AI to predict drug behavior accurately and reduce the time and cost of developing a new drug candidate.
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