Computational Drug Discovery: Molecular Simulation for Medicinal Chemistry

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Computational Drug Discovery: Molecular Simulation for Medicinal Chemistry. Volume 1
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Contents
1. Historical development of computer-aided drug design
	1.1 Background
	1.2 Traditional drug discovery
	1.3 Drug discovery process
	1.4 The birth of computer-aided drug design (CADD)
		1.4.1 Evolution of computer-aided drug design: milestones through the decades
			1.4.1.1 Inception of molecular modeling (1960s)
			1.4.1.2 DENDRAL and BIOSTER systems (early 1970s)
			1.4.1.3 Quantitative structure–activity relationship (QSAR) (1980s)
			1.4.1.4 Molecular dynamics and docking (1990s)
			1.4.1.5 Integration of cheminformatics and high-throughput screening (2000s)
		1.4.2 Advancements in CADD methodologies
			1.4.2.1 Molecular modeling
			1.4.2.2 Molecular dynamics
			1.4.2.3 Structure-based drug design (SBDD)
			1.4.2.4 Homology modeling
			1.4.2.5 Ligand-based drug design (LBDD)
			1.4.2.6 Virtual screening (VS)
		1.4.3 Software and tools for CADD
		1.4.4 Challenges and limitations
			1.4.4.1 Predictive model accuracy
			1.4.4.2 Data quantity and quality
			1.4.4.3 Excessive dependence on computational forecasts
			1.4.4.4 Time and computational cost
			1.4.4.5 Representation of molecular flexibility
	1.5 Commercial medicines that used CADD in their discovery phase
	1.6 Future prospects
	1.7 Conclusion
	References
2. Lead-hit-based methods for drug design and ligand identification
	2.1 Introduction
		2.1.1 High-throughput screening (HTS)
			2.1.1.1 Glivec (imatinib) for chronic myeloid leukemia (CML)
			2.1.1.2 Tamiflu (oseltamivir) for influenza
			2.1.1.3 Xarelto (rivaroxaban) for stroke prevention
			2.1.1.4 Vemurafenib (Zelboraf) for melanoma
		2.1.2 Virtual screening
			2.1.2.1 Falcipain inhibitors for malaria treatment
			2.1.2.2 Inhibitors of HIV integrase
			2.1.2.3 Tubulin inhibitors for cancer therapy
			2.1.2.4 SARS-CoV-2 main protease inhibitors
		2.1.3 Fragment-based drug design
			2.1.3.1 Vemurafenib (Zelboraf) for BRAF-mutant melanoma
			2.1.3.2 Venetoclax (Venclexta) for chronic lymphocytic leukemia (CLL)
			2.1.3.3 Rucaparib (Rubraca) for ovarian and prostate cancer
			2.1.3.4 Erdafitinib (Balversa) for bladder cancer
			2.1.3.5 Gefitinib (Iressa) for lung cancer
		2.1.4 Phenotypic screening
			2.1.4.1 Imatinib (Gleevec) for chronic myeloid leukemia (CML)
			2.1.4.2 Vorinostat (Zolinza) for cutaneous T-cell lymphoma (CTCL)
			2.1.4.3 Osimertinib (Tagrisso) for EGFR-mutated non-small cell lung cancer (NSCLC)
			2.1.4.4 Selinexor (Xpovio) for multiple myeloma
		2.1.5 Natural product screening
		2.1.6 Cheminformatics and QSAR (quantitative structure–activity relationship)
	2.2 Summary
	2.3 Future directions
	Abbreviations
	References
3. Virtual screening tools in ligand and receptor-based drug design
	3.1 Introduction
	3.2 Concept of virtual screening
	3.3 Conclusion
	References
4. State-of-the-art modeling techniques in performing docking algorithms and scoring
	4.1 Introduction
	4.2 Docking algorithms
	4.3 Fast shape matching algorithm (SM)
	4.4 Incremental construction algorithm
	4.5 Genetic algorithm (GA)
	4.6 Monte Carlo method
	4.7 Simulated annealing (SA) method
	4.8 Tabu search (TS)
	4.9 Scoring functions
	4.10 Force-field-based scoring functions
	4.11 Empirical scoring functions
	4.12 Knowledge-based scoring functions
	4.13 Consensus scoring function
	4.14 Docking program
	4.15 AutoDock
	4.16 GOLD
	4.17 DOCK
	4.18 AutoDock Vina
	4.19 Glide
	4.20 FlexX
	4.21 Conclusion
	References
5. Design of computational chiral compounds for drug discovery and development
	5.1 Introduction
	5.2 Background
	5.3 Effect of chirality on biological activity
	5.4 Significance of computational methods in optimization of chiral compounds
		5.4.1 Quantum mechanical studies
		5.4.2 Molecular dynamics
		5.4.3 Molecular mechanics
		5.4.4 Molecular docking
		5.4.5 NMR studies
		5.4.6 Virtual screening
	5.5 Chiral switch
	5.6 Limitations
	5.7 Conclusions
	References
6. Role of integrated bioinformatics in structure-based drug design
	6.1 Introduction
		6.1.1 Computational approaches in drug discovery and development process
			6.1.1.1 Structure-based drug design (SBDD)
				6.1.1.1.1 Docking study
				6.1.1.1.2 Pharmacophore modeling
		6.1.2 Computational tools used for drug design
		6.1.3 Bioinformatics software and databases
		6.1.4 Role of bioinformatics in drug design and discovery process
		6.1.5 Application of bioinformatics tools in drug development
	6.2 Future perspective
	6.3 Conclusion
	List of abbreviations
	References
7. Molecular recognizable tools in X-ray crystallography in computer-aided drug design
	7.1 Introduction
	7.2 Role of X-ray crystallography in CADD
	7.3 Molecular recognizable tools in X-ray crystallography
	7.4 Conclusion
	References
8. Design of target hit molecules using molecular dynamic simulations: special key aspects of GROMACS or Role of molecular dynamic simulations in designing a hit molecule for drug discovery
	8.1 Introduction
		8.1.1 Molecular dynamic simulation
		8.1.2 Brief history
		8.1.3 Why we do a simulation (the basic idea behind simulation)
		8.1.4 Theory of molecular dynamics simulation
			8.1.4.1 Newton’s laws of motion
			8.1.4.2 Potential energy functions
			8.1.4.3 Integration algorithms
				8.1.4.3.1 Verlet algorithm
				8.1.4.3.2 Velocity verlet algorithm
				8.1.4.3.3 Leapfrog algorithm
			8.1.4.4 Ensemble and temperature
			8.1.4.5 Statistical sampling
			8.1.4.6 Boundary conditions
			8.1.4.7 Long-range interactions
		8.1.5 Elements of molecular dynamic simulation
			8.1.5.1 System
			8.1.5.2 Force field
				8.1.5.2.1 AMBER (assisted model building with energy refinement)
				8.1.5.2.2 CHARMM force field
			8.1.5.3 Integration algorithm
			8.1.5.4 Ensemble
				8.1.5.4.1 NVE ensemble
				8.1.5.4.2 NVT ensemble
				8.1.5.4.3 NPT ensemble
				8.1.5.4.4 Grand canonical ensemble
		8.1.6 Types of MD
			8.1.6.1 Canonical ensemble (NVT)
			8.1.6.2 Isothermal-isobaric ensemble (NPT)
			8.1.6.3 Grand canonical ensemble (μVT)
			8.1.6.4 Replica exchange molecular dynamics (REMD)
			8.1.6.5 Umbrella sampling
			8.1.6.6 Steered molecular dynamics (SMD)
			8.1.6.7 Coarse-grained molecular dynamics (CG-MD)
		8.1.7 Software packages for MD
			8.1.7.1 GROMACS
			8.1.7.2 AMBER
			8.1.7.3 NAMD
			8.1.7.4 CHARMM
			8.1.7.5 Desmond
			8.1.7.6 OpenMM
		8.1.8 Impact of molecular dynamics simulations on understanding biological systems
			8.1.8.1 Protein structure and function
			8.1.8.2 Membrane biology
			8.1.8.3 Drug design and discovery
			8.1.8.4 Nucleic acids
			8.1.8.5 Molecular recognition and binding
			8.1.8.6 Drug resistance and mechanisms of action
			8.1.8.7 The role of molecular dynamics simulations in antibody designing
			8.1.8.8 To evaluate the mobility or flexibility of biomolecules
			8.1.8.9 Simulation in drug discovery process
		8.1.9 Exploring molecular dynamics simulation with GROMACS
	8.2 The general steps involved in designing hit molecules using molecular dynamics (MD) simulations
	8.3 Gromacs protocol applied for molecular dynamic simulation targeting as an example of COVID-19
		8.3.1 Results
			8.3.1.1 Root mean square deviation (RMSD)
			8.3.1.2 Root mean square fluctuation (RMSF)
			8.3.1.3 Radius of gyration (Rg)
			8.3.1.4 Hydrogen bond analysis
	References
9. Computational prediction of drug-limited solubility and CYP450-mediated biotransformation
	9.1 Introduction
	9.2 Computational prediction of CYP450-mediated drug toxicity
		9.2.1 Drug-induced toxicity
		9.2.2 Drug-induced liver toxicity
		9.2.3 Role of CADD and artificial intelligence in the prediction of toxicity
	9.3 In silico prediction of CYP450-mediated toxicity
	9.4 CYP450-mediated toxicity prediction based on ML and artificial intelligence
	9.5 Computational prediction of drug limited solubility
	9.6 Conclusion
	Abbreviations used
	References
10. Recent advancement in binding free-energy calculation
	10.1 Introduction
	10.2 Thermodynamic aspect behind the binding free energy
		10.2.1 Gibbs free energy
		10.2.2 Enthalpy
		10.2.3 Entropy
		10.2.4 Boltzmann factor
		10.2.5 Equilibrium constant
		10.2.6 Binding free energy
	10.3 Molecular mechanics and quantum mechanics
		10.3.1 Molecular mechanics
			10.3.1.1 Traid tool concept
			10.3.1.2 The harmonic oscillator model for molecules
			10.3.1.3 Energy due to stretching
			10.3.1.4 Energy due to bending
			10.3.1.5 Energy due to torsional strain
			10.3.1.6 The ab initio potential
			10.3.1.7 Force fields
				10.3.1.7.1 MM2, MM3, MM4, and MMFF94
				10.3.1.7.2 CHARMM
				10.3.1.7.3 AMBER
				10.3.1.7.4 OPLS
	10.4 Quantum mechanics
		10.4.1 The time-independent Schrodinger equation
		10.4.2 The time-dependent Schrödinger equation
	10.5 Binding free energy calculation via scoring function
		10.5.1 Empirical scoring
		10.5.2 Semiempirical scoring
		10.5.3 Force field-based scoring
		10.5.4 Consensus scoring
		10.5.5 Knowledge-based scoring
	10.6 Binding free energy calculation methods
		10.6.1 Extra-precision docking
		10.6.2 Molecular mechanics with generalized Born and surface area solvation (MM-GBSA)
		10.6.3 Molecular dynamics simulation
		10.6.4 Monte Carlo simulation
		10.6.5 Molecular mechanics Poisson–Boltzmann surface area (MMPBSA)
	10.7 Conclusion
	References
11. Role of structural genomics in drug discovery
	11.1 Introduction
	11.2 Structural genomics techniques and approaches
	11.3 Target identification using structural biology
	11.4 Utilizing structural information for rational drug design
	11.5 Structure-based virtual screening approaches
	11.6 Case studies of drugs designed through structural genomics insights
		11.6.1 Case study 1: imatinib (Gleevec®) – chronic myeloid leukemia (BCR-ABL)
		11.6.2 Case study 2: oseltamivir (Tamiflu®) – targeting influenza virus neuraminidase
		11.6.3 Case study 3: raltegravir (Isentress®) – targeting HIV integrase
	11.7 Structure-guided modifications for improved drug candidates
	11.8 Accelerating the drug development process through structural insights
	11.9 Structural genomics and drug resistance
	11.10 Mechanisms of drug resistance at the molecular level
		11.10.1 Intrinsic drug resistance
		11.10.2 Acquired drug resistance
	11.11 Designing drugs to overcome resistance using structural information
	11.12 Combating drug resistance through structure-based approaches
	11.13 Challenges and limitations of structural genomics
	11.14 Data quality and validation issues in structural biology
		11.14.1 Data quality issues
		11.14.2 Data validation issues
		11.14.3 Data quality solutions
		11.14.4 Data validation solutions
	11.15 Overcoming challenges in protein crystallization and sample preparation
		11.15.1 Protein crystallization
		11.15.2 Sample preparation
	11.16 Integrating structural data with other genomic technologies
	11.17 Future prospects and emerging trends
	11.18 Integration of machine learning and artificial intelligence in structural genomics
		11.18.1 Current perspective
	11.19 Expanding structural genomics to nonprotein biomolecules
		11.19.1 Structural genomics of nucleic acids
		11.19.2 Lipid structural genomics
		11.19.3 Structural genomics of small molecules
		11.19.4 Noncoding RNA structures
		11.19.5 Structural genomics of viral genomes
	11.20 Potential impact of cryo-EM on drug discovery
	11.21 Recapitulation of the role of structural genomics in drug discovery
	11.22 Outlook for the future of structural genomics in advancing medicine
	11.23 Conclusion
	References
12. Unlocking therapeutic potential: computational approaches for enzyme inhibition discovery
	12.1 Introduction
	12.2 Ligand-based drug design
		12.2.1 Molecular similarity-based search
			12.2.1.1 Workflow
			12.2.1.2 Applications
		12.2.2 Pharmacophore modeling
		12.2.3 Quantitative structure–activity relationship (QSAR)
			12.2.3.1 Classical or 2D QSAR
			12.2.3.2 3D-QSAR
			12.2.3.3 Multidimensional QSAR
				12.2.3.3.1 4D QSAR
				12.2.3.3.2 5D-QSAR
	12.3 Structure-based computational drug design
		12.3.1 Design of the target structure
		12.3.2 Identification of the ligand binding site
		12.3.3 Molecular docking and scoring functions
		12.3.4 Virtual screening
		12.3.5 De novo drug design
		12.3.6 Molecular dynamics
	12.4 Conclusion
	Abbreviations
	References
13. Role of spectroscopy in drug discovery
	13.1 Introduction
		13.1.1 Identification and validation of suitable drug targets
		13.1.2 Defining drug–receptor interactions
		13.1.3 High-throughput screening
		13.1.4 Structural characterization of lead molecules
		13.1.5 Quantitative analysis of drug metabolism
		13.1.6 Monitoring drug delivery systems
	13.2 Role of IR spectroscopy in drug discovery
		13.2.1 Recent advances in IR spectroscopy to develop biologically active molecules
	13.3 Role of NMR spectroscopy in drug discovery
		13.3.1 Recent advances in NMR spectroscopy to develop biologically active molecules
	13.4 Role of mass spectrometry in drug discovery
		13.4.1 Recent advances in mass spectrometry to develop biologically active molecules
	13.5 Role of X-ray crystallography in drug discovery
		13.5.1 Recent advances in X-ray crystallography to develop biologically active milecules
	13.6 Conclusions
	References
14. Computer-aided design of peptidomimetic therapeutics
	14.1 Introduction
	14.2 Historical insights and current development trends on therapeutic peptides
	14.3 Targeting the undruggable area with therapeutic peptidomimetics
	14.4 Modifying peptides and peptidomimetics to target protein–protein interactions
	14.5 New developments in peptide synthesis
	14.6 Backbone cyclic peptidomimetics’ situation today and their use in drug development
	14.7 The pharmacodynamics and pharmacokinetics of peptidomimetics
	14.8 Peptides that penetrate cells
	14.9 Intracellular peptides as potential therapeutic candidates
	14.10 Therapeutic applications of peptidomimetics
	14.11 The use of venom peptidomimetics in medicine
	14.12 Infectious disease-treating peptidomimetics
	14.13 Medicinal peptidomimetics against parasites
	14.14 Cancer-fighting therapeutic peptidomimetics
	14.15 Computational tools and strategies for peptidomimetics design
	14.16 Drug development and future perspectives on peptide therapeutics
	14.17 Conclusions
	References
15. Developing safer therapeutic agents through toxicity prediction
	15.1 Introduction
	15.2 In silico methods
		15.2.1 Molecular modeling technique
		15.2.2 Ligand-based methods
		15.2.3 Data optimization and modeling
		15.2.4 Quantitative structure activity relationship (QSAR)
		15.2.5 PBPK models
	15.3 Databases
		15.3.1 ADMET-related databases
		15.3.2 Auxiliary databases
		15.3.3 Commonly used software to predict ADMET
	15.4 Conclusions
	References
16. Identifying prominent molecular targets in the fight against drug resistance
	16.1 Introduction
	16.2 Major mechanisms for combating drug resistance
		16.2.1 Mechanisms for combating drug resistance in bacterial infections
		16.2.2 Mechanisms for combating drug resistance in cancer
		16.2.3 Mechanisms for combating drug resistance in viral infections
		16.2.4 Mechanisms for combating drug resistance in parasitic diseases
		16.2.5 Mechanisms for combating drug resistance in fungal infections
		16.2.6 Mechanisms for combating antibiotic-resistant tuberculosis
	16.3 Molecular targets to fight against drug resistance
		16.3.1 Molecular targets to fight against antibacterial drug resistance
		16.3.2 Molecular targets to fight against anticancer drug resistance
		16.3.3 Molecular targets to fight against antiviral drug resistance
		16.3.4 Molecular targets to fight against antiparasitic drug resistance
		16.3.5 Molecular targets to fight against antifungal drug resistance
		16.3.6 Molecular targets to fight against antibiotic resistant tuberculosis
		16.3.7 Molecular targets to fight against resistance in CNS agents
	16.4 Conclusions
	References
Index
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