Virtual screening and drug docking /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Caballero, Julio
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [S.l.] : Academic Press, 2022.
Colección:Annual reports in medicinal chemistry ; v. 59.
Temas:
Drug development > Computer simulation.
Drugs > Structure-activity relationships > Computer simulation.
M�edicaments > D�eveloppement > Simulation par ordinateur.
M�edicaments > Relations structure-activit�e > Simulation par ordinateur.
Drugs > Structure-activity relationships > Computer simulation
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Virtual Screening and Drug Docking
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Can docking scoring functions guarantee success in virtual screening?
  • 1. Introduction
  • 2. Scoring functions
  • 3. Classifications of the scoring functions
  • 3.1. Classical scoring functions
  • 3.1.1. Force field/physics-based scoring functions
  • 3.1.2. Empirical scoring functions
  • 3.1.3. Knowledge-based scoring functions
  • 3.2. Machine learning scoring functions
  • 4. Strengths and weaknesses of classical scoring functions
  • 5. Comparative studies of scoring functions
  • 6. Strategies to improve the performance of scoring functions in virtual screening
  • 7. How to choose a scoring function for virtual screening?
  • 8. Future perspectives in scoring functions
  • 9. Conclusions
  • References
  • Chapter Two: No dance, no partner! A tale of receptor flexibility in docking and virtual screening
  • 1. Introduction
  • 2. Theoretical framework
  • 3. In silico methods to account for flexibility in molecular recognition
  • 3.1. Molecular docking
  • 3.2. MD-based methods for studying ligand-binding events
  • 3.3. Machine learning methods to address protein flexibility
  • 4. Conclusions and perspectives
  • References
  • Chapter Three: Using filters in virtual screening: A comprehensive guide to minimize errors and maximize efficiency
  • 1. Introduction
  • 2. Chemical space and ligand libraries
  • 3. Types of databases for virtual screening
  • 4. Ligand library design and selection
  • 5. Constructing 3D structures-Ligand preparation
  • 6. Druglikeness vs leadlikeness
  • 6.1. Drug-like rules (Lipinski, Ghose, Egan, Muegge, Veber)
  • 6.2. Quantitative estimate of druglikeness (QED)
  • 6.3. Lead-like rules (Oprea)
  • 7. Promiscuous inhibitors and frequent hitters
  • 7.1. PAINS
  • 7.2. Aggregators
  • 7.3. Reactive and toxic functionalities.
  • 8. Knowledge-based filters
  • 9. Pre and post VS tools
  • 10. Machine learning and future directions
  • References
  • Chapter Four: Rational computational approaches to predict novel drug candidates against leishmaniasis
  • 1. Introduction
  • 2. Target-based approaches
  • 2.1. Screening of compounds against Leishmania protein targets using a hybrid MD/docking approach
  • 2.2. Implementation of a machine learning approach to classify druggable kinases in different Leishmania species
  • 2.3. Repositioning of known drugs based on molecular homologue targets in different Leishmania species
  • 3. Ligand-based approaches
  • 3.1. Prediction of different types of toxicity for drug-like molecular candidates
  • 3.2. Prediction of new drug purposes by ligand-based off-target activity predictions
  • 4. Pharmacokinetics simulation
  • 4.1. Population pharmacokinetics simulations of known drugs with potential anti-Leishmania activity
  • 5. Conclusion
  • Acknowledgments
  • References
  • Chapter Five: Virtual screening against Mycobacterium tuberculosis DNA gyrase: Applications and success stories
  • 1. Introduction
  • 2. The Mtb gyrase heterotetramer: Unique catalysis
  • 2.1. Sites of Mtb gyrase inhibition
  • 2.2. Resisting resistance: When gyrase goes rogue
  • 3. Virtual screening in anti-TB drug discovery
  • 4. Structure-guided virtual screening
  • 4.1. Receptor-based pharmacophore modeling and lead optimization against GyrB
  • 4.1.1. From an aminopyrazinamide-bound receptor
  • 4.1.2. Pharmacophore modeling and validation
  • 4.1.3. Pharmacophore model- and docking-based virtual screening
  • 4.1.4. In vitro validation
  • 4.1.5. Lead expansion
  • 4.2. Receptor-based pharmacophore modeling and lead optimization against GyrB
  • 4.2.1. From a pyrrolamide inhibitor-bound receptor
  • 4.2.2. Pharmacophore- and docking-based virtual screening
  • 4.2.3. In vitro validation.
  • 4.2.4. Lead optimization
  • 4.3. Consensus docking and lead optimization: GyrB inhibitor discovery
  • 4.3.1. Docking-based virtual screening and in vitro validation
  • 4.3.2. Lead optimization
  • 5. Ligand-based virtual screening
  • 6. Applications of molecular docking: Designing NBTIs
  • 6.1. Benzimidazole scaffold morphing
  • 6.2. LHS substitutions of NBTIs
  • 7. Docking-based identification of non-fluoroquinolone inhibitors of GyrA
  • 8. Conclusion
  • References.

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