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Having spent nearly 30 years using software in drug discovery, one gets to see and apply many new technologies that are proposed to be ‘game-changers’. The past few years have seen numerous rapid developments in hardware, software, and data such that artificial intelligence (AI) has become an everyday occurrence and is available in cars, phones, and other devices to help us with our everyday tasks. With the emergence of ChatGPT and the many similar generative tools, even the layperson has now heard of ‘generative AI’. This book began just a few years ago as a rather ambitious idea to try to gather a number of the proponents of generative drug discovery in one place while the topic was in its early years. After observing the rapid progress and seeing increasing interest in the field, I saw there were no monographs on the subject up to that point. I thought the topic was rather ‘hot’ then, and it still seems to be having its moment so I hope this book will appear in time to feed the need for scientists to learn more about this topic. The goals of this book are to simply describe the ‘state of the art ‘methods and applications for de novo design of drug candidates using generative chemistry models all the way through to the ethical aspects of this technology. I invited several leading authors from around the world who have published on this topic in the last few years to contribute their chapters. We are honored that these contributors agreed to participate and add their work to create this overview of the topic. Furthermore, to increase the practical appeal of this book to readers, we encouraged the authors to describe and make their respective software accessible. The mix of academic and industrial authors also provides for balance in the topics and also enables exploration of where the field could go in the future. In the intervening time since starting on this project of course, considerably more publications on generative drug discovery have appeared, so this made it a little more difficult to capture everything. But still it should encapsulate a snapshot of where generative drug discovery has been, its potential (without overhyping), limitations, and the prediction of the overall direction it might go in future.