In The News

Below are selected interviews with me or mentions about our work in the news

Nonetheless, an increasing number of groups that blend chemists with computer scientists are banking on continuing advances in deep learning as the drug discovery tool of the future. The use of deep learning in chemistry is less than two years old, says Olexandr Isayev, a computational chemist at the University of North Carolina, Chapel Hill, “but despite that we’re seeing tremendous progress.``
Drug discovery is another promising area. Olexandr Isayev, a research scientist from the University of North Carolina at Chapel Hill, has shown that deep learning algorithms can help train computers to pick out potentially useful drug molecules from hundreds of millions of candidates. Isayev fed data from hundreds of thousands of experiments into his computer systems, and then had his system predict how a molecule might bind to a particular group of proteins. “A typical machine-learning algorithm does one objective function,” he said. “With deep learning you can do multiple optimizations. For example, you might want to maximize binding with this protein but minimize binding with some other protein.”
As with other cutting-edge technologies, hype may also be fueling the progress in—and setting the potential pitfalls for—Insilico Medicine. Olexandr Isayev, an assistant professor at the University of North Carolina whose lab focuses on developing methods of AI-assisted drug discovery, acknowledges that there may be too much excitement over a technology that has yet to provide any material results. “Most published papers, including this one, are purely computational,” he says. “Unfortunately, some predictions could be wrong. I really would like to see the first experimental confirmation of ‘AI-discovered’ molecules.”
Multiple hurdles must still be overcome for AI to fulfill its potential in pharmaceutical R&D. For instance, data sets, even within the same institutions, can be fragmented or stored in incompatible ways, making it difficult for machines to make sense of them unless significant efforts are made to harmonize the data, according to Olexandr Isayev, an assistant professor in machine learning at the University of North Carolina,
Chapel Hill.