The application of Artificial Intelligence in Drug Discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to Artificial Intelligence and Machine Learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
In putting together the book, I reached out to many global thought-leaders in the areas of Drug Discovery and Artificial Intelligence to highlight the important research at the interface. That call was answered by many and this is reflected in the list of the breadth and depth of expertise and experience evidenced in the chapters. The book has contributions from BenevolentAI, but also other leading pharmaceutical and biotech companies, such as AstraZeneca and Novartis. The academic sector is also well represented by the Universities of Cambridge, Oxford, Pompeu Fabra, Bonn, Toronto, Harvard. In addition, more specific institutes like EMBL, ICREA, MIT, Harvard Medical School, The Canadian Institute for Advanced Research, and the Research Centre for Chemoinformatics provided significant contributions. Software and methodology companies are also covered, with chapters from KNIME, MedChemica, and ChemOS. Lastly, and appropriately, The Royal Society of Chemistry is represented with a chapter on deep learning and chemical data.
I hope the book is of great interest and informative to everyone in the scientific community, from those just starting out to those who are more experienced, from those who are weathered experts in Artificial Intelligence and Drug Discovery, to those who are looking how their methods can contribute and benefit, respectively, from these new approaches. Ultimately, I hope this will help to continue the conversation at the interface of many sciences and enable us as a community to contribute to developing new therapeutics to meet unmet human medical need.
Nathan Brown, Director of Chemoinformatics and Computational Chemistry at BenevolentAI