Computational Intelligence In Protein Ligand Interaction Analysis

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

Computer-Aided Drug Design: Drug Discovery, Computational Modelling, and Artificial Intelligence

AI-Assisted Computational Approaches for Immunological Disorders

Based on accelerated computing and advanced algorithms, artificial intelligence (AI) has become a game-changer in diverse fields. In biomedical science, AI models based on machine learning, deep learning, and generative AI, together with upgraded computational and systems biology, provide smart, precise, and personalized medicine and healthcare. In the field of clinical immunology, which deals with intrinsic complicated immune systems for combating immunological disorders, AI technologies have the potential to solve problems based on identification, prediction, simulation, and modeling. AI algorithms may impact sustainable clinical healthcare and future immunology diagnoses. AI-Assisted Computational Approaches for Immunological Disorders explores precision clinical medicine and personalized healthcare in major immunological disorders, such as immunodeficiency, autoimmune diseases, allergies, asthma, cancers, and neurological diseases. It analyzes clinical healthcare big data, genomics, and high-throughput multiple omics based on single-cell and high-dimensional data for their technical advances, clinical applications, challenges, and future directions. Covering topics such as cell therapy, reducing complications, and drug development, this book is an excellent resource for immunologists, biologists, healthcare practitioners, professionals, researchers, scholars, academicians, and more.