Artificial intelligence (AI) and machine learning (ML) have emerged over the last decade as the cutting-edge technologies most expected to revolutionise the pharmaceutical R & D industry. Revolutionary developments in computer technology and the concomitant evaporation of earlier limits on the collection/processing of enormous amounts of data are contributing factors. Meanwhile, the price of developing and delivering new medicines to the market for patients has skyrocketed. Despite these challenges, the pharmaceutical sector is interested in AI/ML methods because of their predictivity, automation, and the efficiency boost that is projected as a result. Over the last 15–20 years, ML techniques have been increasingly used in the drug development process. Clinical trial design, conduct, and analysis are the most recent areas of drug research to see beneficial disruption from AI/ML. Due to the rising dependence on digital technology in the execution of clinical trials, the COVID-19 pandemic could further drive the employment of AI/ML in clinical trials. Getting through the associated buzzwords and noise is crucial as we progress towards a future where AI/ML is more integrated into R & D. Similarly crucial is the acknowledgement that the scientific method is still relevant for concluding evidence. By doing so, we can better evaluate the potential benefits of AI/ML in the pharmaceutical industry and make well-informed decisions on the best use. The purpose of this book is to clarify important ideas, provide examples of their application, and provide a well-rounded perspective on how to best use AI/ML techniques in research and development.