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Computational Drug Discovery and Design Second Edition 2024 [Hardback]

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  • Formāts: Hardback, 356 pages, height x width: 254x178 mm, weight: 889 g, 1 Illustrations, black and white; XI, 356 p. 1 illus., 1 Hardback
  • Sērija : Methods in Molecular Biology 2714
  • Izdošanas datums: 08-Sep-2023
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1071634402
  • ISBN-13: 9781071634400
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Computational Drug Discovery and Design Second Edition 2024Palielināt
  • Formāts: Hardback, 356 pages, height x width: 254x178 mm, weight: 889 g, 1 Illustrations, black and white; XI, 356 p. 1 illus., 1 Hardback
  • Sērija : Methods in Molecular Biology 2714
  • Izdošanas datums: 08-Sep-2023
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 1071634402
  • ISBN-13: 9781071634400
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781071634400.html
Keywords:
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.
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.- Proteinligand 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.