Artificial intelligence model finds potential drug molecules a thousand times faster

Artificial intelligence model finds potential drug molecules a thousand times faster

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

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 answer: novemdecillion, or 1060. This gargantuan number prolongs the drug development process for fast-spreading diseases like COVID-19 because it is far beyond what existing drug design models can comput…

Artificial intelligence model finds potential drug molecules a thousand times faster

In a paper that can be introduced on the Worldwide Convention on Machine Studying (ICML), MIT researchers developed a geometrical deep-learning mannequin