Alzheimer’s. Cancer. Parkinson’s. These devastating diseases are just a few of the hundreds that scientists are struggling to cure with new medicines in the face of soaring drug-discovery costs and testing times. Now, artificial intelligence is showing the potential to be a faster, more efficient way to find and develop new drugs. A growing number of companies and university researchers are tackling some of medicine’s toughest problems by using AI computing to predict which drug molecules are most likely to be effective treatments. “This is a revolution in the pharmaceutical industry,” said Alex Zhavoronkov, CEO of Insilico Medicine, which uses GPU-accelerated deep learning to target cancer and age-related illnesses.
BenevolentBio aims to reinvent drug discovery by using deep learning and natural language processing to understand and analyze vast quantities of bioscience information — patents, genomic data and the more than 10,000 publications uploaded daily across all biomedical journals and databases. “Humans alone cannot process all this information to advance scientific research,” Hunter said. Cells believed to play a role in the progression of ALS. BenevolentBio used deep learning to find two possible treatments. Image courtesy of Oregon State University. BenevolentBio’s deep learning software, powered by the NVIDIA DGX-1 AI supercomputer, ingests and analyzes the information to find connections and propose drug candidates.
