welcome

The path that lead to this book began in pursuit of my doctorate. This experience entailed my first exposure to many of the discussed concepts and was quite confusing and tortuous (to say the least). What are machine learning (ML) and deep learning (DL)? How do I connect these concepts to practical applications in chemistry or drug development? How does a computer even interpret what a molecule is in the same way that it parses a piece of text or an image? Thankfully, following this path gave me a wealth of experience that I can now share with you!

Using case studies drawn from my work in industry and academia, we will progress deeper into the fundamentals of drug discovery and the theory surrounding applicable methods in ML and DL. We will explore, visualize, and analyze chemical data across different formats and representations via open-source cheminformatics tools, such as RDKit. Much of the material is also applicable to domains outside of drug discovery, such as modeling best practices and using PyTorch to build, train, evaluate, fine-tune, and save models.