コンピュータ創薬・設計（第２版） Computational Drug Discovery and Design (Methods in Molecular Biology) （2ND）

Gore, Mohini (EDT)/ Jagtap, Umesh B. (EDT)

Springer-Verlag New York Inc.（2023/11発売）

• 製本 Hardcover:ハードカバー版／ページ数 284 p.
• 言語 ENG
• 商品コード 9781071634400

Full Description

This second edition provides new and updated methods and techniques for identification of drug target, binding sites prediction, high- throughput virtual screening, lead discovery and optimization, conformational sampling, prediction of pharmacokinetic properties using computer-based methodologies. Chapters also focus on the application of the latest artificial intelligence technologies for computer aided drug discovery. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.

 

Authoritative and cutting-edge, Computational Drug Discovery and Design, Second Edition aims to effectively utilize computational methodologies in discovery and design of novel drugs.

Contents

Computer-Aided Drug Discovery and Design - Recent Advances and Future Prospects.-  Virtual Screening Process - A Guide in Modern Drug Designing.- Molecular dynamics as a tool for virtual ligand screening.- Antiviral Drug Target Identification and Ligand Discovery.- GRAMM webserver for protein docking.- Protein-ligand blind docking using CB-Dock2.- Applications of Molecular Dynamics Simulations in Drug Discovery.- Molecular dynamics simulation-based prediction of glycosaminoglycan interactions with drug molecules.- Mining chemogenomic spaces for prediction of drug-target interactions.- Expanding the landscape of amyloid sequences with CARs-DB: a database of polar amyloidogenic peptides from disordered proteins.- Accelerating molecular dynamics simulations for drug discovery.- Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach. -Recent Deep-Learning Applications to Structure-BasedDrug Design.- Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots.- AI driven enhancements in drug screening and optimisation.- Applications of big data and AI-driven technologies in CADD (computer-aided drug design).- Artificial Intelligence in ADME Property Prediction.- Accelerating the discovery and design of antimicrobial peptides with artificial intelligence.