Data Science in Chemistry - Large Language Models, Chemometrics, Predictive and Generative Design with Jupyter

Data science has become a core discipline of modern chemistry. With the rapid growth of available data, the fourth paradigm of science has taken shape, built on three pillars: computer science, mathematics, and the specialist domain ‘chemistry’. This book shows how to put data science to work: Chemists learn how to use Python, cheminformatics, and machine learning to gain new insight from their data. Data scientists find use cases from pharma and chemistry and learn how their methods apply to the domain.

Data science itself is moving on. Large language models are changing not only the tools but also the way chemists look at their data and their work. The second edition follows this shift: it adds a new chapter on generative AI, covering foundation models, prompt engineering, retrieval-augmented generation, fine-tuning, and AI agents, and a catalog of design patterns for data science in chemistry.