Automating Drug Discovery With Machine Learning

The traditional path of drug development is lengthy, expensive, and suffers from high failure rates – scientists test millions of molecules, however, only a handful progress to preclinical or clinical testing. Embracing innovation, particularly automated technologies, is essential to reduce the complexity associated with drug discovery and circumvent the high cost and time spent bringing a medicine to market. The subsequent sections will highlight examples of how ML can be used for drug repurposing and to discover novel antibiotics. The application of ML strategies to enhance image-based profiling and accelerate drug discovery will also be discussed. Drug discovery is often thought of as a complex jigsaw puzzle where connecting workflows and data are essential pieces.