Computational Intelligence in Protein-Ligand Interaction Analysis

Bing Wang, Peng Chen, Jun Zhang

• Publisher: Academic Press
• Year: 2024
• eBook ISBN: 9780128244357
• Edition: 0
• Topic: Biological Sciences
• Subtopic: Molecular Biology
• Index: Biological Sciences

Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing 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. - 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

Table of contents

• Computational Intelligence in Protein-Ligand Interaction Analysis
• Chapter 1 Random forest method for predicting protein ligand–binding residues
• Chapter 2 Encoders of protein residues for identifying protein–protein interacting residues
• Chapter 3 Ensemble method for the Identification of hotspot residues from protein sequences
• Chapter 4 Predicting protein interaction sites from unlabeled sample information based on a semi-supervised approach
• Chapter 5 An XGBoost-based model to predict protein–protein interaction sites
• Chapter 6 Evolutional algorithms and their applications in protein long-range contact prediction
• Chapter 7 A two-stage peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry data
• Chapter 8 Predicting drug–target interactions with electrotopological state fingerprints and amphiphilic pseudo amino acid composition
• Chapter 9 Ensemble learning–based prediction on drug–target interactions
• Chapter 10 Convolutional neural networks for drug–target interaction prediction
• Chapter 11 Ensemble learning methods for drug-induced liver injury identification
• Chapter 12 Database construction for mutant protein interactions
• Chapter 13 Predicting drug efficacy using a linear programming computational framework
• Index