Computer aided drug design (CADD) : from ligand-based methods to structure-based approaches /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Rudrapal, Mithun, Egbuna, Chukwuebuka
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Diego : Elsevier, 2022.
Colección:Drug Discovery Update.
Temas:
Drugs > Design > Computer simulation.
Drugs > Design > Data processing.
M�edicaments > Conception > Simulation par ordinateur.
M�edicaments > Conception > Informatique.
Drugs > Design > Computer simulation
Drugs > Design > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Computer Aided Drug Design (CADD): From Ligand-Based Methods to Structure-Based Approaches
  • Copyright
  • Contents
  • Contributors
  • Chapter 1: Introduction to drug design and discovery
  • Chapter outline
  • 1. Definition and concept of drug design and discovery
  • 2. Historical perspectives of drug discovery
  • 3. Process, strategies, and stages of drug discovery and development
  • 3.1. Discovery phase
  • 3.2. Preclinical phase
  • 3.3. Clinical phase
  • 3.4. Approval and postapproval phases
  • 4. Traditional and modern approaches to drug discovery and development
  • 4.1. Virtual screening
  • 4.2. High-throughput screening
  • 4.3. Phenotypic screening
  • 4.4. Structure-based drug design
  • 4.5. Fragment-based drug design
  • 4.6. Ligand-based drug design
  • 5. Rational drug design (RDD) and CADD
  • 5.1. Structure-based drug design (SBDD)
  • 5.1.1. Molecular docking
  • 5.1.2. Molecular dynamics
  • 5.2. Ligand-based drug design (LBDD)
  • 5.2.1. Quantitative structure-activity relationship (QSAR)
  • 5.2.2. Pharmacophore modeling and similarity search
  • 5.2.3. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction
  • 6. Conclusion
  • References
  • Chapter 2: Fundamental considerations in drug design
  • Chapter outline
  • 1. Fundamentals of rational drug design (RDD)
  • 1.1. Rational drug design
  • 1.2. Structure-based drug design (SBDD)
  • 1.3. Ligand-based drug design (LBDD)
  • 2. Concepts of physicochemical properties
  • 2.1. Structural properties and stereochemistry
  • 2.2. Drug receptors and receptor theories
  • 2.2.1. Occupation theory
  • 2.2.2. Rate theory
  • 2.2.3. Induced fit theory
  • 2.3. Pharmacokinetics and pharmacodynamics
  • 2.4. SARs and QSARs
  • 2.5. Prodrugs and drug metabolism
  • 2.6. Metabolite antagonism and enzyme inhibition
  • 2.7. Nucleic acid-based drug design
  • 2.8. Lead compounds.
  • 2.9. Peptidomimetics and analog design
  • 2.10. Reverse pharmacology and drug repurposing strategies
  • 3. Fundamentals of computer-aided drug design (CADD)
  • 3.1. Structure-based drug design (SBDD)
  • 3.1.1. Docking
  • 3.1.2. Molecular dynamics (MD)
  • 3.1.3. Structure-based pharmacophore modeling
  • 3.2. Ligand-based drug design (LBDD)
  • 3.2.1. Similarity search
  • 3.2.2. QSAR modeling
  • 3.2.3. Ligand-based pharmacophore modeling
  • 3.3. Virtual screening techniques
  • 3.3.1. Structure- or target-based virtual screening
  • 3.3.2. Successful applications of virtual screening
  • 3.4. ADME analysis and measures of drug-likeness
  • 4. Conclusion
  • References
  • Chapter 3: Ligand-based drug design (LBDD)
  • Chapter outline
  • 1. Introduction
  • 2. Random and nonrandom screening
  • 2.1. Drug metabolism studies
  • 2.2. Serendipity method
  • 2.3. Clinical observations
  • 3. Drug discovery process
  • 3.1. Ligand-based drug design (LBDD)
  • 3.2. Structure-based drug design (SBDD)
  • 4. Combinatorial chemistry
  • 4.1. Unbiased library
  • 4.2. Biased library
  • 4.2.1. Solid-phase synthesis
  • 4.2.2. Solution-phase synthesis
  • 5. Lead modifications and optimization approaches
  • 5.1. Pharmacophore
  • 5.2. Structure-activity relationships (SARs)
  • 6. Stereochemistry of drug molecules
  • 6.1. Importance in drug action
  • 6.2. Stereoselectivity in drug-receptor interaction
  • 6.3. Stereospecific aspects in drug design
  • 6.4. Stereochemistry in biological processes
  • 6.5. Significance of stereoselectivity
  • 7. Bioisosterism
  • 7.1. Need and use of bioisosteric replacements
  • 7.2. Classification of bioisosterism11-14
  • 7.2.1. Classical bioisosteres
  • Monovalent bioisosteres
  • Divalent bioisosteres
  • Trivalent atoms or groups
  • Tetrasubstituted atoms
  • Ring equivalents
  • 7.2.2. Nonclassical bioisosteres
  • 8. Drug metabolism2,16,17
  • 8.1. Objectives.
  • 8.2. Prodrugs18
  • 8.2.1. Objectives
  • 8.2.2. Classifications of prodrugs
  • Based on the type of carrier moiety
  • Carrier-linked prodrugs
  • Bioprecursor prodrugs
  • Based on cellular site of bioactivation
  • Type I
  • Type II
  • 8.2.3. Essential functionalities associated with prodrugs scheming
  • 8.3. Retrometabolism-based drug design (RMDD)
  • 8.3.1. Principle
  • 8.3.2. Soft drug design
  • 8.3.3. Chemical delivery system
  • 9. Virtual high-throughput screening (vHTS)
  • 9.1. Tools for virtual high-throughput screening (vHTS)
  • 9.1.1. Octopus
  • 9.1.2. PyRx
  • 9.1.3. Raccoon2
  • 9.2. Techniques for virtual high-throughput screening (vHTS)
  • 9.2.1. Ligand-based vHTS
  • 9.2.2. Structure-based vHTS
  • 9.3. Lipinskis rule
  • 9.4. Veber rule
  • 9.5. ADMET screening
  • 9.6. Toxicity prediction
  • 9.7. Docking-based virtual screening (DBVS)
  • 9.8. Pharmacophore-based virtual screening (PBVS)
  • 9.8.1. Water thermodynamics
  • 9.8.2. Binding free energy calculations
  • Binding kinetics
  • Binding site accessibility and drug size
  • Conformational fluctuations
  • Electrostatics
  • Hydrophobicity and water
  • Residence time
  • Optimizing residence time
  • 10. Conclusion
  • Acknowledgment
  • References
  • Chapter 4: Quantitative structure-activity relationships (QSARs)
  • Chapter outline
  • 1. QSAR: Fundamentals and historical background
  • 1.1. Definition
  • 1.2. Historical background
  • 2. Hammett equation
  • 3. Hansch-Fujita model
  • 4. Free and Wilson method
  • 5. Protocols for managing a QSAR study
  • 6. Conditions for the validity of the model
  • 6.1. Regarding the variable selection
  • 6.2. Regarding the variable validation
  • 6.3. Regarding the model validation
  • 6.4. Regarding the amount of variables
  • 6.5. Regarding the biological validation
  • 6.6. Regarding model recycling
  • 7. 3D-QSAR
  • 7.1. CoMFA
  • 7.2. CoMSIA
  • 7.3. SOMFA.
  • 7.4. GRID/GOLPE
  • 7.5. HASL
  • 7.6. COMPASS
  • 8. Case study
  • 9. Conclusion
  • References
  • Chapter 5: Fundamentals of molecular modeling in drug design
  • Chapter outline
  • 1. Fundamentals of computational chemistry
  • 2. Basic concepts of quantum mechanics
  • 3. Sketch approach, conversion of 2D structures in 3D form, and generation of 3D coordinates
  • 4. Molecular dynamics simulation and its components
  • 4.1. Force fields
  • 4.2. Geometry optimization
  • 4.3. Energy minimization
  • 4.4. Conformational search
  • 4.5. Genetic algorithms
  • 4.6. Monte Carlo simulation
  • 4.7. Artificial intelligence methods
  • 4.8. Pharmacophore identification and molecular modeling
  • 5. Molecular recognition in drug design
  • 6. Thermodynamic consideration of drug designing
  • 6.1. Methods of thermodynamic measurement for bimolecular interactions
  • 6.1.1. Direct method
  • 6.1.2. Indirect method by Vant Hoff analysis
  • 6.2. Physical basis of intermolecular interaction
  • 6.2.1. Total energy of intermolecular interactions
  • 6.2.2. Estimating individual group components in ligand receptor interactions and cooperativity and thumb rules
  • 7. Conclusion and future scope
  • References
  • Chapter 6: Pharmacophore modeling in drug design
  • Chapter outline
  • 1. Introduction
  • 2. Computer-aided drug design
  • 3. Pharmacophore concept
  • 3.1. Ligand-based pharmacophore
  • 3.2. Structure-based pharmacophore (SBP)
  • 4. Pharmacophore model-based virtual screening (VS)
  • 5. Pharmacophore elements and representation
  • 6. Generation of pharmacophore models from receptor-ligand complex
  • 7. Applications of pharmacophores in ADME-Tox
  • 7.1. Pharmacophore-guided drug target identification
  • 7.2. Multitargets by pharmacophore
  • 7.3. Possible applications of multitarget ligands
  • 7.3.1. Drug resistance
  • 7.3.2. Prospective drug repositioning
  • 8. Conclusion.

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