Computational Intelligence in Protein-Ligand Interaction Analysis

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 INDICE: 1. Computational intelligence methods in protein-ligand interactions2. Random forest method for predicting protein ligand-binding residues3. Encoders of protein residues for identifying protein-protein interacting residues4. Identification of hot spot residues in protein interfaces from protein sequences and ensemble methods5. Semi-supervised prediction of protein interaction sites from unlabeled sample information6. Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm7. Evolutional algorithms and their applications in protein long-range contact prediction8. A novel robust geometric approach for modelling protein-protein interaction networks9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis10. Ensemble learning-based prediction on drug-target interactions11. Convolutional neural networks for drug-target interaction prediction12. Ensemble learning methods for drug-induced liver injury identification13. Database construction for mutant protein interactions14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy

• ISBN: 978-0-12-824386-2
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 310
• Fecha Publicación: 01/11/2022
• Nº Volúmenes: 1
• Idioma: Inglés