Artificial Neural Network for Drug Design, Delivery and Disposition - 1st Edition - ISBN: 9780128015599, 9780128017449

Artificial Neural Network for Drug Design, Delivery and Disposition

1st Edition

Authors: Munish Puri Yashwant Pathak Vijay Sutariya Srinivas Tipparaju Wilfrido Moreno
eBook ISBN: 9780128017449
Hardcover ISBN: 9780128015599
Imprint: Academic Press
Published Date: 22nd October 2015
Page Count: 440
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Description

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.

Key Features

  • 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

Readership

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.

Table of Contents

  • 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
      • 3. Bayesian-Regularized Artificial Genetic Neural Networks
      • 4. Model's Validation
      • 5. Datasets Sources and Preparation
      • 6. BRGNN in Drug Design QSAR
      • 7. Conclusions
    • 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

Details

No. of pages:
440
Language:
English
Copyright:
© Academic Press 2016
Published:
Imprint:
Academic Press
eBook ISBN:
9780128017449
Hardcover ISBN:
9780128015599

About the Author

Munish Puri

Affiliations and Expertise

Visiting Fellow, National Cancer Institute, NIH, Bethesda, Maryland, USA

Yashwant Pathak

Affiliations and Expertise

M.Pharm., EMBA, MS (Conflict Management), PhD, Professor and Associate Dean for Faculty Affairs, College of Pharmacy, University of South Florida Health, Tampa, FL

Vijay Sutariya

Affiliations and Expertise

PhD, Assistant Professor, Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL

Srinivas Tipparaju

Affiliations and Expertise

PhD, MPharm, Assistant Professor, Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL

Wilfrido Moreno

Affiliations and Expertise

PhD, PE, Professor, Department of Electrical Engineering, College of Engineering, University of South Florida, Tampa, FL