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[null,"1.","Record Nr.",null,"UNINA9911019543303321"]
[null,null,"Autore",null,"Malviya Rishabha"]
[null,null,"Titolo",null,"Drug Delivery Systems Using Quantum Computing"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"9781394159338"]
[{"$nulls":4},"1394159331"]
[{"$nulls":4},"9781394159321"]
[{"$nulls":4},"1394159323"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (476 pages)"]
[null,null,"Disciplina",null,"615.6028563843"]
[null,null,"Soggetti",null,"Quantum computing"]
[{"$nulls":4},"Drug delivery systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1 Quantum Computational Concepts and Approaches in Drug Discovery, Development and Delivery -- 1.1 Introduction -- 1.2 Algorithms and QC in Pharma -- 1.2.1 Algorithms -- 1.2.2 Supervised Learning -- 1.2.3 Unsupervised Learning -- 1.2.4 Multi-Task Neural Networks -- 1.2.5 Graph Convolution -- 1.3 Potential of QC in Drug Discovery -- 1.3.1 Target Recognition and Validation -- 1.3.2 Production and Validation of Hits -- 1.3.3 Lead Optimization -- 1.3.4 Clinical Trials -- 1.3.5 Linking and Generating Data -- 1.4 QC and Drug Delivery -- 1.5 QC in Drug Delivery Modalities -- 1.5.1 Computational Approach Towards Nano Particulate Drug Delivery -- 1.5.2 Computational Approach for Bone Drug Delivery -- 1.5.3 Computational Approach for Polymeric Drug Delivery -- 1.5.4 Computational Approach for Microsphere Drug Delivery -- 1.5.5 Computational Approach for Dendrimer-Based Drug Delivery -- 1.5.6 Computational Approach for Carbon Nanotube-Based Drug Delivery -- 1.6 QC Applications in Pharma Industry -- 1.7 Challenges or Prospects -- 1.8 Conclusion -- References -- Chapter 2 Quantum-Enabled Drug Discovery Process -- 2.1 Introduction -- 2.2 Usual Challenges in Drug Designing and Discovery -- 2.2.1 Commercial Challenge -- 2.2.2 Modification or Transitional Challenges -- 2.2.3 Unexplored Areas "]
[{"$nulls":4},"Under Classical Computational Techniques -- 2.3 Medicinal Chemistry Through Quantum Mechanics -- 2.3.1 Underappreciation of Chemical Interactions in Protein-Ligand Complexes -- 2.3.2 Non-Classical Hydrogen Bonding -- 2.3.3 π-π Stacking -- 2.3.4 Hydrophobic Bond Interactions -- 2.3.5 Coordination from Water -- 2.3.6 Explicit Water -- 2.3.7 Implicit Water -- 2.4 Interaction Analysis -- 2.4.1 Fragment Interaction Energy."]
[{"$nulls":4},"2.4.2 Binding Free Energy -- 2.4.3 Fragment Molecular Orbital (FMO) Process and Analysis -- 2.5 Geometric Optimization -- 2.5.1 Analyzing Gradients -- 2.6 GAMESS: A Computational Technique for Biochemical Simulations -- 2.6.1 Introduction to Biochemical Simulations -- 2.6.2 Parametrizing Quantum Mechanics Method for Simulations -- 2.6.3 Quantum Mechanics and Molecular Mechanics Associated with GAMESS -- 2.6.4 Introduction to QuanPol -- 2.6.5 QuanPol: Covalent Boundary Treatment -- 2.6.6 GO and Harmonic Vibration Frequency -- 2.6.7 Molecular Dynamics Simulation -- 2.6.8 Free Energy Perturbation (Deviation of Energy) Simulation -- 2.6.8.1 QuanPol Process Umbrella Sampling -- 2.6.8.2 QuanPol Process Thermodynamic Integration (TI) -- 2.6.9 Setting Up Valuation and Calculation -- 2.6.10 Molecular Modeling and Visualization Software -- 2.7 Conclusion -- References -- Chapter 3 Quantum Computing and Its Promise in Drug Discovery -- 3.1 Introduction -- 3.1.1 Need for Quantum Computing -- 3.2 Quantum Computing's Types, Applications, Generality, and Power -- 3.2.1 Quantum Annealer -- 3.2.2 Analog Quantum -- 3.2.3 Universal Quantum -- 3.3 Drug Discovery and Quantum Computing -- 3.3.1 A Brief History of Drug Discovery -- 3.3.2 Modern Drug Discovery -- 3.4 Role of Quantum Computing in Drug Discovery -- 3.5 Quantum Computing Methodology in Drug Discovery -- 3.5.1 Target Identification and Validation -- 3.5.2 Hit Generation and Validation -- 3.5.3 Lead Optimization -- 3.5.4 Data Linkage and Generation -- 3.5.5 Clinical Trials -- 3.5.6 Molecular Formations -- 3.6 Examples of Companies Using Quantum Theory to Accelerate Drug Discovery -- 3.6.1 Aqemia -- 3.6.2 Hafnium Labs -- 3.6.3 Kuano -- 3.6.4 Menten AI -- 3.6.5 Pharmacelera -- 3.6.6 PharmCADD -- 3.6.7 Polaris Quantum Biotech -- 3.6.8 ProteinQure -- 3.6.9 Riverlane -- 3.6.10 Roivant Discovery -- 3.6.11 XtalPi."]
[{"$nulls":4},"3.6.12 Zapata Computing -- 3.7 Advantages of Quantum Computing -- 3.8 Applications of Quantum Computing in Drug Discovery and Development -- 3.8.1 A Future View of QC and Drug Discovery -- 3.8.2 Current Developments in QC and Drug Discovery -- 3.9 Conclusion -- References -- Chapter 4 Exploring Nano-Based Therapeutics by Quantum Computational Modeling -- 4.1 Introduction to Nano-Based Therapeutics -- 4.2 Introduction to Quantum Computational Modeling with Respect to Nano-Based Therapeutics -- 4.2.1 Particle-Based Models -- 4.2.2 Continuum-Based Models -- 4.3 Exploration of Nano-Based Therapeutics -- 4.4 Design and Development of Nano-Based Therapeutics -- 4.4.1 Prediction of Solubility -- 4.4.2 Prediction of Permeability -- 4.4.3 Selection of Components and Optimization of Formulation of Nano-Based Therapeutics -- 4.4.3.1 Prediction of Therapeutic Loading -- 4.4.4 Prediction of Therapeutic Release or Leakage -- 4.4.5 Prediction of the Pharmacokinetic Profile of Nano-Based Therapeutics -- 4.4.5.1 Prediction of Absorption -- 4.4.5.2 Prediction of Distribution (Protein Corona Formation) -- 4.4.5.3 Prediction of Metabolism -- 4.4.5.4 Prediction of Excretion -- 4.4.6 Understanding Protein Corona Formation -- 4.4.7 Understanding the Interaction of Nanosized Carrying Objects with Bio-Membranes -- 4.4.8 Prediction of the Pharmacodynamic Profile of Nano-Based Therapeutics -- 4.4.9 Prediction of Adverse Drug Reactions and "]
[{"$nulls":4},"Nanotoxicity -- 4.4.10 Design of Target-Oriented Nano-Based Therapeutics -- 4.5 Conclusion -- References -- Chapter 5 Application of Quantum Computational Simulation in Drug Delivery Strategies with Carbon Nanotubes -- 5.1 Introduction -- 5.2 Properties of CNTs -- 5.2.1 Electrical Properties of CNTs -- 5.2.2 Elastro-Mechanical Properties of CNTs -- 5.2.3 Thermal Properties of CNTs -- 5.2.4 Optical Properties of CNTs."]
[{"$nulls":4},"5.3 Functionalization of CNTs -- 5.3.1 Covalent Functionalization -- 5.3.2 Noncovalent Functionalization of CNTs -- 5.3.3 Encapsulation Inside CNTs -- 5.3.4 \"Defect\" Functionalization -- 5.4 Significance of CNT in Drug Delivery -- 5.5 Overview of CNT-Based Drug Delivery -- 5.6 Pharmacokinetics of CNTs -- 5.6.1 Absorption -- 5.6.2 Distribution -- 5.6.3 Metabolism and Excretion -- 5.7 Biosafety of Carbon Nanotube -- 5.7.1 Mechanism of CNT Toxicity -- 5.7.2 Scenario to Bypass Carbon Nanotube Toxicity -- 5.8 Quantum Computational -- 5.8.1 Structure-Based Drug Design Methods (SBDD) -- 5.8.2 Ligand-Based Drug Design Methods (LBDD) -- 5.9 Various Simulation Approaches in Drug-CNTs Interaction -- 5.9.1 QM Approaches -- 5.9.1.1 Ab Initio Approach -- 5.9.1.2 Semiempirical Approach -- 5.9.1.3 Hartree-Fock (HF) Approach -- 5.9.2 Molecular Dynamics (MD) Approaches -- 5.9.3 Monte Carlo (MC) Simulation Approaches -- 5.9.4 Hybrid Approaches -- 5.9.4.1 MD and QM Approaches -- 5.9.4.2 MM and QM Approaches -- 5.10 Applications of Quantum Computational Methods -- 5.10.1 Applications of Quantum Computational Methods in DDS -- 5.10.2 Applications of Quantum Computational Methods in Nanobiosensors -- 5.11 Conclusion -- References -- Chapter 6 Quantum Computation Approach for Nanotechnology-Based Targeted Drug Delivery Systems -- 6.1 Introduction -- 6.2 The Types of Quantum Computers -- 6.2.1 Scalable Quantum Computers -- 6.2.2 Noisy Intermediate-Scale Quantum Devices -- 6.2.3 Analog Quantum Devices -- 6.3 Role of QC in Computer-Aided Drug Design -- 6.4 Development of Molecular Formulations -- 6.4.1 QC-Based Development of Nanocarriers -- 6.4.2 QC in Biosensor -- 6.4.3 QC-Based Targeted Drug Delivery -- 6.4.4 Target Identification -- 6.4.5 Target Validation -- 6.4.6 Identification of Hit and Its Validation -- 6.4.7 Optimization of Lead."]
[{"$nulls":4},"6.5 Data Generation, Interpretation, and Co-Relation -- 6.6 Role of QC in Clinical Trials -- 6.7 Future Prospects -- 6.8 Conclusion -- References -- Chapter 7 Role of Quantum Computing Simulations in Targeted Drug Delivery of Liposomes -- 7.1 Introduction -- 7.2 Liposomes -- 7.3 Liposome Classification -- 7.3.1 Based on Preparation Methods -- 7.3.2 Based on Compositional and Structural Characteristics -- 7.4 Methods of Liposome Preparation -- 7.5 Drug Loading Method -- 7.5.1 Passive Loading Technique -- 7.5.1.1 Sonication -- 7.5.1.2 French Pressure Cell -- 7.5.1.3 Freeze-Thawed Liposomes -- 7.5.1.4 Solvent Evaporation with Ether Injection -- 7.5.1.5 Alcohol Infusion -- 7.5.1.6 Method of Reverse Phase Evaporation -- 7.5.1.7 Removal of Non-Encapsulated Material with Detergent -- 7.5.2 Active Loading Technique -- 7.5.2.1 Pro-Liposome -- 7.5.2.2 Lyophilization -- 7.6 Newer Approaches to Liposomes -- 7.6.1 Stealth Liposomes (Improving Circulation Time) -- 7.6.2 Improving Elasticity (Transferosomes) -- 7.6.3 Ethosomes (Improving Skin Penetration) -- 7.6.4 Pharmacosomes (Improvement in Medication Delivery for Poorly Soluble Medicines) -- 7.6.5 Nebulized Liposomes and Stimuli-Responsive Liposomes -- 7.7 Quantum Computing -- 7.8 Computational Modeling for Drug Delivery Process -- 7.9 Correlation Between Quantum Computing Simulation and Targeted Delivery of Liposomes -- 7.10 Computational Simulation of Lipid Membranes -- "]
[{"$nulls":4},"7.11 Properties Measured by Simulation -- 7.11.1 Mechanical and Structural Properties -- 7.11.2 Dynamic Properties -- 7.11.3 Molecule Permeation -- 7.12 Liposomal Drug Delivery System -- 7.13 Experimental Techniques and Role of Computational Simulation in Liposomal Drug Delivery System -- 7.13.1 Lipids Membrane -- 7.13.2 Size, Surface Charge, and Zeta Potential -- 7.13.3 Morphology and Lamellarity."]
[{"$nulls":4},"7.14 Computational Simulation Study of Liposomes."]
[null,null,null,"Sommario/riassunto","This book explores the integration of quantum computing into drug delivery systems, aiming to revolutionize the pharmaceutical industry by enhancing drug discovery processes. Edited by experts Rishabha Malviya, Sonali Sundram, and Dhanalekshmi Unnikrishnan, it discusses quantum computational concepts, methodologies, and their applications in drug design and delivery. The book covers various topics including quantum algorithms, nano-based therapeutics, carbon nanotube drug delivery systems, and the challenges and prospects of quantum computing in pharmaceuticals. It serves as a comprehensive resource for researchers and professionals in the fields of pharmacy, biotechnology, and computer science, providing insights into the potential of cutting-edge technologies in improving therapeutic outcomes."]