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Indigo Ai in Chemistry From Data to Discovery: A Practical Guide to Machine Learning For Chemical Sciences | Erin Mills Town Centre
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AI in Chemistry bridges the gap between traditional chemical training and the data-driven methods reshaping drug discovery, materials science, and process chemistry. It is not a theoretical survey of artificial intelligence, but a hands-on, code-driven journey that equips chemists and chemical engineers to harness modern machine learning directly on chemical data.Built around a complete set of ready-to-run Jupyter notebooks, the book introduces each concept by answering a practitioner's most immediate questions: How does a computer "see" a molecule? Why did a model make a specific prediction, and can we trust it? How can we ask an algorithm to invent new molecules rather than just screen existing ones? Through ten carefully structured chapters, readers move from the fundamentals of chemical representation to the frontiers of generative design, large language models, and self-driving laboratories.
AI in Chemistry bridges the gap between traditional chemical training and the data-driven methods reshaping drug discovery, materials science, and process chemistry. It is not a theoretical survey of artificial intelligence, but a hands-on, code-driven journey that equips chemists and chemical engineers to harness modern machine learning directly on chemical data.Built around a complete set of ready-to-run Jupyter notebooks, the book introduces each concept by answering a practitioner's most immediate questions: How does a computer "see" a molecule? Why did a model make a specific prediction, and can we trust it? How can we ask an algorithm to invent new molecules rather than just screen existing ones? Through ten carefully structured chapters, readers move from the fundamentals of chemical representation to the frontiers of generative design, large language models, and self-driving laboratories.
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