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Cheminformatics with Python
Molecular Descriptors, Similarity Search, QSAR Modeling, and Screening Workflows
Engleski
84 b
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Reactive Publishing
Cheminformatics with Python is a practical guide to using Python for molecular data analysis, compound comparison, QSAR modeling, and screening workflows.
Designed for students, researchers, data scientists, and computational chemistry practitioners, this book introduces the core methods used to represent, analyze, and model chemical structures in modern cheminformatics. It focuses on applied workflows rather than abstract theory, showing how molecular descriptors, fingerprints, similarity measures, and predictive models can be used to support compound analysis and early-stage discovery work.
Inside, readers will learn how to work with molecular datasets, calculate descriptors, compare compounds using similarity search, build QSAR models, evaluate predictive performance, and structure screening workflows using Python-based tools. The book emphasizes reproducible analysis, clear modeling decisions, and practical interpretation of results.
Topics include:
Molecular representations and chemical data formats
Descriptor calculation and feature engineering
Molecular fingerprints and similarity search
QSAR modeling concepts and workflows
Compound screening and prioritization
Model validation and performance evaluation
Practical Python-based analysis patterns
Whether you are entering cheminformatics from chemistry, bioinformatics, data science, or pharmaceutical research, this book provides a structured path for applying Python to molecular analysis and computational discovery workflows.
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Engleski
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