Over the past century, drug discoveryDrug discovery has evolved from a largely serendipitous process to a systematic program. Nevertheless, after a period of rapid advances in new “miracle drugs”, the pace of discovery of novel therapeutic molecules has not kept up with the increase in pharmaceutical R&D spending. It now typically takes over a billion dollars to bring a new drug to market. Machine learning methods are now routinely used in the pharmaceutical industry to analyze the large amount of data generated by robotic high-throughput biological assays, leading to hopes for shortening of the drug discoveryDrug discovery pipeline. The advent of this data-rich era has given rise to a new data-driven, rather than the traditional hypothesis-driven paradigm in drug design. This trend is amplified by recent generative deep learning methods, which offer the promise of rapidly and cost-effectively generating new bioactive molecules within a desired range of properties. Application of machine learningMachine learning in drug design and informatics methods in materials discovery has been slower to take off, but has witnessed remarkable progress in recent years, with the availability of large materials databases, deep generative methods, robotic synthesis and characterization tools. This chapter reviews recent developments in the application of artificial intelligence and machine learning techniques to the design of novel materials and therapeutic drugs, highlights some ongoing concerns and anticipates possible future trends.