Regina Barzilay’s office at MIT affords a clear view of the Novartis Institutes for Biomedical Research. Amgen’s drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world’s leading researchers in artificial intelligence, hadn’t given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs?
The problem is that human researchers can explore only a tiny slice of what is possible. It’s estimated that there are as many as 1060 potentially drug-like molecules—more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.
Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule’s structure affects its properties. They synthesize and test countless variants, and most are failures. “Coming up with new molecules is still an art, because you have such a huge space of possibilities,” says Barzilay. “It takes a long time to find good drug candidates.”