Zhimin Zhang, Hongmei Lu, Ming Wen

Broschiertes Buch

Cheminformatics with Python

Beschreibung

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.

Autor

Zhimin Zhang is an Associate Professor of Analytical Chemistry at Central South University, PR China. He received his Bachelor and Doctoral degrees from Central South University. His main research interests are chemometrics and cheminformatics, machine learning and deep learning, high-resolution mass spectrometry and its resolution methods, Raman spectroscopy and its resolution methods, and chemometric software development. In recent years, he has hosted 4 national and provincial research projects, including the National Natural Science Foundation of China (NSFC) Youth Fund, National Major Scientific Instrument and Equipment Development Special Task, Hunan Provincial Natural Science Foundation Youth Fund, and National Postdoctoral Fund. He has also cooperated with B&W Tek, Shimadzu, ExxonMobil, National University of Defense Technology, Yunnan Institute of Tobacco Agricultural Science, and other enterprises and research institutions in the fields of data analysis and software development. He has published more than 100 SCI papers in Analytical Chemistry, Bioinformatics, Analytica Chimica Acta, Analyst, Chemometrics and Intelligent Laboratory Systems, Journal of Chemometrics, and other journals. He has been engaged in the development of chemometric software for analytical instrument data processing for a long time and has developed several sets of chemometric software and obtained 10 computer software copyrights. The developed chemometric software BWIQ (http://bwtek.com/products/bwiq/) is sold worldwide together with B&W Tek Raman and NIR spectrometers. He is currently an invited reviewer for Analytical Chemistry, Chemometrics and Intelligent Laboratory Systems, Analytica Chimica Acta, Journal of Chromatography A, and Analyst.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 512
• Erscheinungstermin: 1. Mai 2026
• Englisch
• ISBN-13: 9780443291869
• ISBN-10: 0443291861
• Artikelnr.: 75536775