The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? Millions? Billions? Trillions? The answe
MIT researchers developed a geometric deep learning model that is more accurate and over 1,000 times faster at finding potential drug-like molecules than the fastest state-of-the-art computational models, reducing the chances and costs of failures in an industry where 90 percent of drug candidates fail clinical trials.
Artificial intelligence model finds potential drug molecules a thousand times faster scienceblog.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from scienceblog.com Daily Mail and Mail on Sunday newspapers.
MIT researchers have developed a deep learning model that can rapidly predict the likely 3D shapes of a molecule given a 2D graph of its structure. This technique could accelerate drug discovery by narrowing down the number of molecules pharmaceutical researchers need to test in lab experiments.