This handbook provides an overview of the most common chemoinformatics algorithms in a single source. It explains how algorithms and graph theory are applied to chemical problems, such as structure-activity/property predictions. The book describes virtual screening techniques, docking methods, inverse-QSAR methods, de novo design algorithms, sequence alignment algorithms, and classical machine learning algorithms. Along with reviewing open source software and databases, it also explores the development and validation of ...
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This handbook provides an overview of the most common chemoinformatics algorithms in a single source. It explains how algorithms and graph theory are applied to chemical problems, such as structure-activity/property predictions. The book describes virtual screening techniques, docking methods, inverse-QSAR methods, de novo design algorithms, sequence alignment algorithms, and classical machine learning algorithms. Along with reviewing open source software and databases, it also explores the development and validation of QSAR models and covers applications in combinatorial library design, synthesis design, biological network inference, and systems biology.
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