Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its…Read more
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Artificial Neural Network for Drug Design, Delivery and Disposition
provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help researchers more effectively design, develop, and deliver successful drugs.
This book illustrates how to use ANN methodologies and models with the intent to treat diseases like breast cancer, cardiac disease, and more. It contains the latest cutting-edge research, an analysis of the benefits of ANN, and relevant industry examples. As such, this book is an essential resource for academic and industry researchers across the pharmaceutical and biomedical sciences.
Written by leading academic and industry scientists who have contributed significantly to the field and are at the forefront of artificial neural network (ANN) research
Focuses on ANN in drug design, discovery and delivery, as well as adopted methodologies and their applications to the treatment of various diseases and disorders
Chapters cover important topics across the pharmaceutical process, such as ANN in structure-based drug design and the application of ANN in modern drug discovery
Presents the future potential of ANN-based strategies in biomedical image analysis and much more
Industry and academic researchers working in the pharmaceutical sciences, including those involved in computational drug design, drug discovery, drug delivery, biomedicine, neuroscience, bioengineering and bioinformatics.
Dedication
Foreword
Preface
Section 1. Basics of ANN: Concept and Strategy in Drug Design
Chapter 1. Introduction to Artificial Neural Network (ANN) as a Predictive Tool for Drug Design, Discovery, Delivery, and Disposition: Basic Concepts and Modeling
1. Artificial Neural Network
2. ANN as a Classifier
3. ANNs in Drug Delivery and Disposition
4. Applications of ANN Modeling in Drug Delivery and Pharmaceutical Research
5. ANN Applications in Analytical Data Analysis and Structure Retention Relationship Methodology
6. ANN Application in Preformulation and Optimization of Pharmaceutical Formulation
7. In Vitro In Vivo Correlation
8. ANN Applications in Predicting Blood–Brain Barrier Permeability
9. Quantitative Structure-Activity Relationships (QSAR) and Quantitative Structure-Property Relationships (QSPR)
Chapter 2. The Role of Artificial Neural Networks on Target Validation in Drug Discovery and Development
1. Introduction
2. Basics of ANN
3. Target Validation in Drug Discovery and Development
4. ANN in Target Validation in Drug Discovery and Development
5. Target Validation and Neural Networks
6. ANNs in ADME, Toxicity, and Drug Delivery
7. Summary
Chapter 3. Computational Basis of Neural Elements
1. Introduction
2. Overview of Artificial Neural Networks
3. Historical Perspectives
4. Characteristics of the Artificial Neural Network
5. Brain Networks
6. Challenges of Artificial Neuronal Networks
7. Neurons
8. Neuronal Processes
9. Synaptic Connectivity
10. Adaptation in the Brain
11. Microneural Network Themes
12. Lateral Inhibition
13. Sensory Transduction
14. Feedforward Excitation
15. Feedforward Inhibition
16. Feedback Excitation
17. Feedback Inhibition
18. Feedback/Recurrent Excitation
19. Feedback/Recurrent Inhibition
20. Neural Development
21. Genetic and Molecular Elements of Neural Development
22. Cortical Synaptogenesis
23. Cortical Organization
24. Cerebral Cortex
25. Sensory Cortex
26. Primary Sensory Cortex
27. Secondary Sensory Cortex
28. Motor Cortex
29. Association Cortex
30. Cerebral Dominance
31. Functional Basis of Cortical Development
32. Conclusions
Chapter 4. Genetic Algorithm Optimization of Bayesian-Regularized Artificial Neural Networks in Drug Design
1. Introduction
2. Genetic Algorithm Implementations in Drug Design QSAR
Chapter 5. Neurobiological Computation and Neural Networks
1. Cognitive Neuroscience and New Technologies
2. Cells in the Nervous System and Information Processing
3. Neuroglias and Biological Synthesis of Information
4. Genetics and Cognition
5. Complexity of Information
6. Information Processing
7. The Brain and Complex Problem Resolutions
8. Emotions and Problem Solving
9. Intercerebral Connectivity and Emotions
10. Attention and Time in Information Processing
11. Importance of the Problem Resolution Process
12. Problem Solving and Movement
13. Relevance of Information
14. Memory and Problem Solving
15. Unconscious Reasoning and Complex Problem Solving
Section 2. Basics and Application of ANN in Drug Discovery
Chapter 6. Application of Artificial Neural Networks in Modern Drug Discovery
1. Introduction
2. Structure of an ANN
3. Information Processing in ANNs
4. Types of ANNs
5. Features of ANNs
6. Applications of ANNs in Drug Discovery
7. Conclusion
Chapter 7. Impact and Challenges of Chemoinformatics in Drug Discovery
1. Introduction
2. Conclusion
Chapter 8. Impact of Artificial Neural Networks in QSAR and Computational Modeling
1. Introduction
2. ANN-Based Modeling Studies
3. Concluding Remarks
Chapter 9. Data Mining in Drug Discovery and Design
1. Introduction
2. From High-Throughput Screening to Virtual Screening
3. Quantitative Structure–Activity Relationship
4. Pharmacokinetic and Pharmacodynamics
5. Conclusion
Chapter 10. Artificial Neural Networking in Controlled Drug Delivery
1. Introduction to Controlled Drug Delivery Systems
2. What are ANNs?
3. How to Use ANN and Other Related Techniques to Design CRDDS
4. Applications of ANNs in the Design of CRDDS
5. Limitations of ANN
6. Conclusion
Section 3. ANN in Drug Delivery
Chapter 11. Artificial Neural Networks in Drug Transport Modeling and Simulation–I
1. Introduction
2. Why Are ANNs, Modeling and Simulation of Drug Transport Useful?
3. ANNs and Their Helping Hand in the Discovery of New Drugs
4. Applicability to Research—Review of Scholarly Works
5. The Use of ANNs during Formulation Design
6. Conclusions
Chapter 12. Artificial Neural Networks in Drug Transport Modeling and Simulation–II
1. Introduction
2. Overview of Artificial Neural Networks
3. Modeling Smart Drug Transport Systems for Controlled-Release Technology
4. Simulation of STS and CRT Systems Using ANNs
5. Designing ANNs for Drug Transport Models
6. Conclusion
Chapter 13. Artificial Neural Network as Helping Tool for Drug Formulation and Drug Administration Strategies
1. Introduction and a Brief History
2. ANNs: A Brief Overview
3. Advantages of ANNs over Conventional Statistics
4. Development of an ANN Model
5. Application of ANNs in Pharmaceutical Formulation Development
6. Software Used for ANN Application
7. ANNs as a Helping Tool for Drug Administration Strategies
8. Conclusion
Chapter 14. ANN in Pharmaceutical Product and Process Development
1. Introduction
2. Background
3. ANNs in Pharmaceutical Product and Process Development
4. Conclusion
Section 4. ANN in Drug Disposition
Chapter 15. Classic Formal Logic and Nonclassical Logics: Basis of Research on Neural Networks
1. Logic and Neural Networks
2. Nonformal Logic and Reasoning
3. Classical Formal Logic
4. Formal Logic Principles
5. Propositional Logic and Reasoning
6. Binary Logic and Decision-Making
7. Predicate Logic and Reasoning
8. Polyadic Predicates and Relationship Systems
9. Notions of Relationships of Higher Order
10. Properties of Systems of Relations
11. Representation: Summary of Classical Formal Logic
12. Nonclassical Formal Logic: A New Reasoning Process
13. Extensive Logics of Classical Nonformal Logic
14. Disruptive Logics of Classical Formal Logic
15. Nonclassical Logics, Math, and Neural Networks
16. To Conclude
17. Representation: Summary of Nonclassical Logics
Chapter 16. Neural Networks and Computational Complexity
1. P–NP Problems
2. Heuristics and Problem-Solving
3. Metaheuristics and Complex Problems
4. Synthetic Representation of Metaheuristics
Chapter 17. Adaptive Modeling and Intelligent Control of a Sodium Nitroprusside Delivery System
1. Introduction
2. Generalized Fuzzy Neural Network
3. Adaptive Modeling of the SNP Delivery Systems
4. G-FNN-Based Control of the SNP Delivery Systems
5. Simulation Results
6. Conclusions
Section 5. ANN in Various Applications in Medicine
Chapter 18. Recent Advances of Biochemical Analysis: ANN as a Tool for Earlier Cancer Detection and Treatment
1. Introduction
2. ANNs in Cancer Diagnostics
3. Design and Use of ANNs in a Clinical Setting
4. Conclusion and Future Perspectives
Chapter 19. Role of an Artificial Neural Network Classifier in Nuclear Pleomorphic Feature Analysis of Histopathological Images of Breast Cancer
1. Introduction
2. Digital Pathology
3. Nuclear Pleomorphism
4. Artificial Neural Networks
5. ANN Classifier
6. Classifier Optimization
7. Results and Discussion
8. NeuroSolutions Classifier
9. Proposed Idea
10. Future Work
11. Conclusion
Chapter 20. Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling
1. Introduction
2. What Are ANNs?
3. ANNs in Pharmacokinetic and Pharmacodynamic Modeling
4. Clinical Application of ANNs
5. Conclusion
Index
No. of pages: 440
Language: English
Edition: 1
Published: October 15, 2015
Imprint: Academic Press
Hardback ISBN: 9780128015599
eBook ISBN: 9780128017449
MP
Munish Puri
Affiliations and expertise
Visiting Fellow, National Cancer Institute, NIH, Bethesda, Maryland, USA
YP
Yashwant Pathak
Yashwant Pathak, is Professor and Associate Dean for the Faculty Affairs at the College of Pharmacy, at the University of South Florida. Tampa, USA. His area of research is in health care education, nanotechnology, drug delivery systems and nutraceuticals.
Affiliations and expertise
Professor and Associate Dean, Faculty Affairs, College of Pharmacy, University of South Florida, Tampa, USA
VS
Vijay Kumar Sutariya
Affiliations and expertise
PhD, Assistant Professor, Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL
ST
Srinivas Tipparaju
Affiliations and expertise
PhD, MPharm, Assistant Professor, Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL
WM
Wilfrido Moreno
Affiliations and expertise
PhD, PE, Professor, Department of Electrical Engineering, College of Engineering, University of South Florida, Tampa, FL
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