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Full Description
Machine learning and deep learning have now been widely used in cheminformatics, and programming skills are becoming a must for most chemists. Python has become an invaluable and highly popular open-source programming language that is ideally suited for data analysis and artificial intelligence in the field. Cheminformatics with Python provides a ground-up, practical introduction that will help the reader make effective use of the software, demonstrating how to use Python to write efficient cheminformatics programs and how to apply it to solve practical chemical problems. The book contains four main parts: programming, data, methods, and applications. In the programming section, a brief introduction to Python language and related scientific computing, cheminformatics, machine learning, and deep learning packages is provided, building knowledge from the ground up. In the data section, a systematic study of the representation of instrumental data, representation of molecular structures, and common chemical databases is given. In the methods section, analytical signal processing, multivariate calibration, multivariate resolution, classical machine learning, and deep learning methods are introduced in detail. The application section then looks at case studies of successful applications of cheminformatics in analytical chemistry, metabolomics, drug discovery, materials science, and other research areas which are demonstrated in detail. Finally, in the supporting appendix section, the necessary mathematical, statistical, and information theory-related theories in the main text are provided, and practical tips such as code editors and source code management are also included. Online coding materials on GitHub and an individual Jupyter notebook for each chapter further support practical learning. Cheminformatics with Python is written primarily for senior undergraduate students, graduate students, post-docs, and professors primarily in the field of computational and analytical chemistry who are harnessing AI, as well as those in medicinal and biochemistry or materials science applying cheminformatics in drug discovery, materials design, or metabolomics research.
Contents
1. Introduction
Part I: Python for Cheminformatics
2. Python Basics
3. Python Packages
Part II: Data and Databases
4. Representation of Instrumental Signals
5. Representation of Molecules
6. Databases in Chemistry
Part III: Methods
7. Instrumental Signal Processing
8. Multivariate Calibration and Resolution
9. Manipulation of Molecular Structures
10. Classic Machine Learning Methods
11. Deep Learning Methods
Part IV: Applications
12. Cheminformatics in Analytical Chemistry
13. Cheminformatics in Metabonomics
14. Cheminformatics in Drug Discovery
15. Cheminformatics in Materials Science
Appendices
A: Necessary Knowledge of Mathematics
B: Editors and IDEs
