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? 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 compute. To put it into perspective, the Milky Way has about 100 thousand million, or 108, stars. In a paper that will be presented at the International Conference on Machine Learning (ICML), MIT researchers developed a geometric deep-learning model called EquiBind that is 1,200 times faster than one of the fastest existing computational molecular docking models, QuickVina2-W, in successfully binding drug-like molecules to proteins.