AI identifies drug candidate in weeks

Abstract

The artificial intelligence start-up Insilico Medicine has used machine learning to find credible drug candidates in a matter of weeks (Nat. Biotechnol. 2019, DOI: 10.1038/s41587-019-0224-x). Experts say it’s an important demonstration of what machine learning can do in drug discovery, but it isn’t a revolution. Insilico Medicine has been showing off its progress in teaching computers to find new drugs since its founding in 2014. The company’s latest effort involves generative reinforcement learning, a technique that uses rewards to guide an algorithm as it searches for molecules that satisfy its goals. In this case, the algorithm was hunting for small molecules that are inhibitors of discoidin domain receptor 1 (DDR1), a kinase linked to fibrosis. The researchers trained their algorithm using databases of known DDR1 inhibitors, kinase inhibitors, nonkinase inhibitors, and patent-protected molecules. Using measures of novelty and DDR1 inhibition, the algorithm proposed 30,000 potential drugs. This group was filtered