Computer Aided Drug Design (CADD): From Ligand-Based Methods to Structure-Based Approaches

Front Cover
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
References
Chapter 7: Structure-based drug design (SBDD)
Chapter outline
1. Computer-aided drug design
2. Structure-based drug design (SBDD)
2.1. Overview of the processes involved in structure-based drug design (SBDD)39
2.2. Examples of SBDD
2.3. Case study of SBDD
3. Molecular docking
3.1. Various models pertaining to molecular docking
3.2. Classification of molecular docking systems
3.3. Docking based screening
3.4. Molecular docking steps and procedure/docking protocol
4. Molecular dynamics
4.1. Applications of MD
4.2. Binding free energy calculations with MMGBSA/PBSA
4.3. Molecular dynamics simulation using DESMOND
4.4. Case study
5. Conclusion
References
Chapter 8: Recent advances in CADD
Chapter outline
1. Introduction
2. Role of informatics in drug discovery
2.1. Chemoinformatics in drug discovery
2.1.1. Database generation
2.1.2. Chemical descriptors
2.1.3. Role of informatics in drug development
Identification of problem (disease)
Target validation
Identification and optimization of lead
Preclinical and clinical trials
Approval and commercialization
2.1.4. Role of omics in drug discovery
Genomics
Transcriptomics
Proteomics
Metabolomics
3. Databases used in drug discovery
3.1. Recent trends of ADMET
3.2. Prediction of physicochemical properties
3.2.1. Solubility
3.2.2. Ionization constant
3.2.3. Lipophilicity
3.3. Prediction of ADMET properties
3.3.1. Absorption
3.3.2. Distribution
3.3.3. Metabolism
3.3.4. Excretion
3.3.5. Toxicity
4. Fragment-based drug design
4.1. Library design for FBDD
4.2. Strategies in fragment-based drug design
4.2.1. Fragment growing
4.2.2. Fragment linking
4.2.3. Fragment merging
4.3. General consideration on fragment-based drug design
5. Receptor-based de novo design
5.1. Methods involved in de novo drug design
6. Nucleic acid-based drug design (NABDD)
7. Advances in drug designing
7.1. Similarity searching
7.2. Artificial intelligence (AI)
7.3. Machine learning (ML)
7.4. Data mining
7.5. Network analysis and system biology tools
7.6. Data analysis tools
8. In silico approaches in drug repurposing
8.1. Knowledge-based approach
8.2. Target/structure-based approach
8.3. Ligand-based approach
8.4. Pathway/network-based approach
8.5. Signature-based approach
8.6. Targeted mechanism-based approach
8.7. Examples of successful drug repurposing
9. Design of biologics and protein drug design
9.1. Space for computational biologics design
9.2. In silico protein designing
9.3. Boundary between us and protein designing
10. Conclusion
Acknowledgment
References
Chapter 9: Limitations and future challenges of computer-aided drug design methods
Chapter outline
1. Introduction
2. Limitations of computer-aided drug design methods (CADD)
2.1. Limitations of structure-based drug design (SBDD)
2.1.1. Limitations with protein selection and validation
2.1.2. Lack of accurate scoring function
2.1.3. Limitations related to model interpretation
2.2. Limitations of ligand-based drug design (LBDD) methods
2.2.1. Limitations of QSAR
2.2.2. Limitations of pharmacophore based virtual screening
Limitations in scoring function
Dependency on the precomputed database
Lack of a universal method for the development of pharmacophore
3. Challenges in computer-aided drug design methods
3.1. Challenges in SBDD
3.1.1. Challenges in homology modelling25-27
3.1.2. Challenges related to the role of water molecules
3.2. Challenges in LBDD33
3.3. Challenges associated with the force fields
3.4. Challenges associated with the scoring function
3.5. Challenges in ADME/T prediction
3.6. Challenges in molecular dynamics (MD) simulation
4. Future development in CADD
4.1. Nature-A source of learning in drug discovery
4.2. Precision medicine
4.3. Combining CADD with other techniques of design, synthesis, and testing
5. Conclusion
Abbreviations
References
Index
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