Computational Intelligence in Protein-Ligand Interaction Analysis
  • Release Date : 15 November 2021
  • Publisher : Academic Press
  • Genre : Science
  • Pages : 310 pages
  • ISBN 13 : 0128243864
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Download or read book entitled Computational Intelligence in Protein-Ligand Interaction Analysis by author: Bing Wang which was release on 15 November 2021 and published by Academic Press with total page 310 pages . This book available in PDF, EPUB and Kindle Format. The volume and speed of scientific data has transformed the life sciences, leading to an acceleration in bioinformatics, computational biology, and quantitative biology. In turn, researchers in bioinformatics and computational biology now require a suite of computational, mathematical, and statistical skills in order to handle large amounts of data. Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognising protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving scientific explanations for biological data through computational intelligence. Fourteen chapters offer a comprehensive guide to protein interaction data, and computational intelligence methods for protein-ligand interactions, demonstrating ways computational tools and techniques can be applied for effective research on protein-ligand interaction. Presents a guide to computational techniques for protein-ligand interaction analysis Guides researchers in developing advanced computational intelligence methods for the protein-ligand problem Identifies appropriate computational tools for various problems Demonstrates the use of advanced techniques such as vector machine, neural networks, and machine learning Offers the computational, mathematical and statistical skills researchers need