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.

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.