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Structure-Based Drug Design [electronic resource] / edited by Marcelo A. Marti, Adrian Gustavo Turjanski, Dario Fernández Do Porto.

Contributor(s): Material type: TextSeries: Computer-Aided Drug Discovery and Design ; 2Publisher: Cham : Springer International Publishing : Imprint: Springer, 2024Edition: 1st ed. 2024Description: IX, 256 p. 80 illus., 70 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031691621
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • R850.A1-854
  • QH315-320
Online resources:
Contents:
Prioritizing Drug Targets in Pathogenic Bacteria by Harnessing Structural Biology, Metabolic Analysis, and Omics Data Integration -- Regulatory small RNAs as antimicrobial drug targets -- Riboswitches as antimicrobial targets -- Pre-selection of compounds for lead identification in virtual screening campaigns -- Docking and Bias Docking -- Molecular Dynamics and its Significance in Drug Discovery -- Deep Learning Strategies for Enhanced Molecular Docking and Virtual Screening -- Integrating computational approaches from non-synonymous sequence variations to molecular structure for drug repositioning targeting the SARS-CoV-2 Spike protein.
In: Springer Nature eBookSummary: This volume focuses on target-oriented approximations to drug discovery, including target selection, binding pocket detection, and current uses and variants of molecular dynamics and molecular docking. The primary audience is PhD and graduates working in the field of molecular biology, structural biology, pharmaceutical sciences.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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Prioritizing Drug Targets in Pathogenic Bacteria by Harnessing Structural Biology, Metabolic Analysis, and Omics Data Integration -- Regulatory small RNAs as antimicrobial drug targets -- Riboswitches as antimicrobial targets -- Pre-selection of compounds for lead identification in virtual screening campaigns -- Docking and Bias Docking -- Molecular Dynamics and its Significance in Drug Discovery -- Deep Learning Strategies for Enhanced Molecular Docking and Virtual Screening -- Integrating computational approaches from non-synonymous sequence variations to molecular structure for drug repositioning targeting the SARS-CoV-2 Spike protein.

This volume focuses on target-oriented approximations to drug discovery, including target selection, binding pocket detection, and current uses and variants of molecular dynamics and molecular docking. The primary audience is PhD and graduates working in the field of molecular biology, structural biology, pharmaceutical sciences.

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