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Computational Neuropharmacology

Fundamentals and Clinical Aspects

Edited by Bhupendra Prajapati, Alok Tripathi, Rishabha Malviya and Lucy Mohapatra
Copyright: 2025 2025   |   Status: Published
ISBN: 9781394242443  |  Hardcover  |  
510 pages
Price: $$225 USD
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One Line Description
The book gives comprehensive insights into the cutting-edge intersection of computational methods and neuropharmacology, making it an essential resource for understanding and advancing medication for neurological and psychiatric disorders.

Audience
This book is a valuable resource for computer scientists, engineers, researchers, clinicians, and students, providing a detailed understanding of the computational tools that are changing the developing field of neuropharmacology, leading the future of medication discovery and development for neurological and psychiatric illnesses by combining modern computational approaches with neuropharmacological research.

Description
Computational Neuropharmacology is an in-depth exploration of the convergence of computational methods with neuropharmacology, a science concerned with understanding pharmacological effects on the nervous system. This volume explores the most recent breakthroughs and potential advances in computational neuropharmacology, providing an extensive overview of the computational tools that are transforming medication discovery and development for neurological and psychiatric illnesses. Fundamental principles of computational neuropharmacology, descriptions of molecular-level interactions and their consequences for modern neuropharmacology, and an introduction to theoretical neuroscience are highlighted throughout this resource. Additionally, this study addresses computational attitudes in counseling psychology to improve therapeutic procedures through data-driven insights. Computational psychiatry uses computational technologies to bridge the gap between the molecular basis and clinical symptoms of psychiatric diseases.
This volume covers computational approaches to drug discovery in neurohumoral transmission and signal transduction, Parkinson’s disease, epilepsy, and Alzheimer’s disease, and the use of molecular docking and machine learning in drug development for neurological disorders. It also discusses the use of computational methods to uncover potential treatments for autism spectrum disorder, depression, and anxiety.

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Author / Editor Details
Bhupendra Prajapati, PhD, is a professor in the Department of Pharmaceutics, Shree S.K. Patel College of Pharmaceutical Education and Research, Ganpat University, Gujarat, India, with over 20 years of research and teaching experience. He has published over 100 research and review papers in national and international journals and 20 book chapters, edited three books, and serves as an editor and reviewer of several journals. In addition to this work, he has published two Indian patents and has three applications under evaluation.

Alok Shiomurti Tripathi, PhD, is a professor and the Head of the Department of Pharmacology, Era College of Pharmacy, Era University, Gujarat, India, with over 14 years of teaching and research experience. He has published 40 research papers in various national and international journals, five book chapters, and one book and serves as a reviewer and editorial board member for several international journals. His research focuses on diabetes and its complications, drug interaction, and neurodegenerative disorders.

Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, India, with over 12 years of research and teaching experience. He has published 28 books and over 150 research papers in national and international journals and has been granted 10 patents, with an additional 40 under review. His research interests include formulation optimization, nanoformulation, targeted drug delivery, artificial intelligence in healthcare, and characterization of natural polymers as pharmaceutical excipients.

Lucy Mohapatra, PhD, is an assistant professor in the Department of Pharmacology, Amity Institute of Pharmacy, Amity University Uttar Pradesh, India, with eight years of teaching and research experience. She has published over 17 research papers in various national and international journals, five book chapters, and four patents and serves as a reviewer and editorial board member for several journals. Her research interests include metabolic disorders, mitochondrial disorders, pharmacology, and pathophysiology.

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Table of Contents
Foreword
Preface
Part 1: Fundamentals of Computational Neuropharmacology
1. Basic Principles of Computational Neuropharmacology: Neuroscience Meeting Pharmacology
Lucy Mohapatra, Alok S. Tripathi, Deepak Mishra, Alka and Sambit Kumar Parida
Abbreviations
1.1 Introduction
1.2 Basics of Computational Neuropharmacology
1.3 Multiple Aspects of Computational Neuropharmacology
1.3.1 Drug Databases
1.3.2 Computational fMRI
1.3.3 Compartmental Simulations of Neuronal Electrophysiology
1.3.4 The Concept of System Thinking Makes an Appearance in Neuropharmacology
1.3.5 Quantitative Systems Pharmacology
1.4 Recent Developments in Computational Neuropharmacology
1.4.1 Compartmental Simulations of Neuronal Electrophysiology
1.4.2 Quantitative Structure–Activity Relationships (QSAR) for Alzheimer’s Disease Treatment
1.4.3 Composite Machine Learning Algorithms for Schizophrenia Treatment
1.4.4 Discovery and Evaluation of Dual Target Ligands for Parkinson’s Disease
1.4.5 Utilizing Structure-Based Drug Design for Neurological Disorder Therapies
1.4.6 Structural Modeling of Voltage-Gated Sodium Ion Channel from Anopheles gambiae
1.5 Limitations of Computational Neuropharmacology
1.6 Conclusion
References
2. Neuropharmacology in the Molecular Epoch
Neelakanta Sarvashiva Kiran, Chandrashekar Yashaswini and Bhupendra G. Prajapati
List of Abbreviations
2.1 Introduction
2.2 History of Neuropharmacology
2.3 Neurochemical Interactions
2.4 Molecular Pharmacology of Neuronal Receptors
2.4.1 Acetylcholine Receptors
2.4.2 Serotonin Receptors
2.4.3 Dopamine Receptors
2.4.4 γ-Aminobutyric Acid Receptors
2.4.5 Norepinephrine Receptors
2.4.6 Glutamate Receptors
2.5 Neuropharmacological Drugs
2.6 Impact of Biotechnology of Neuropharmacology
2.7 Future Research and Perspectives
2.8 Conclusion
Acknowledgments
References
3. Basics of Theoretical Neuroscience
Anil P. Dewani, Deepak S. Mohale, Alok S. Tripathi and Naheed Waseem A. Sheikh
List of Abbreviations
3.1 Introduction
3.1.1 Neurons and Neuronal Network
3.1.1.1 Neurons
3.1.1.2 Neural Network
3.2 Properties of Neurons and Neuronal Signaling
3.2.1 Properties of Neurons
3.2.2 Neuronal Signaling
3.3 Recording Neuronal Responses
3.4 Neural Encoding and Neuronal Decoding
3.4.1 Neuronal Encoding
3.4.2 Neural Decoding
3.5 Neuronal Network Models
3.6 Learning and Synaptic Plasticity
3.7 Conclusion
References
4. In Silico Modeling of Drug–Receptor Interactions for Rational Drug Design in Neuropharmacology
Princy Shrivastav, Bhupendra Prajapati, Chandni Chandarana and Parixit Prajapati
Abbreviations
4.1 Introduction
4.2 Drug–Receptor Interactions
4.2.1 Overview of Receptors and Their Role in Drug Action
4.2.2 Types of Receptor and Ligand Interactions
4.2.2.1 Enzyme–Substrate Interaction
4.2.2.2 Ligand-Gated Ion Channels
4.2.2.3 Hormone–Receptor Interaction
4.2.2.4 Ligand-Activated Transcription Factors
4.2.2.5 Receptor Tyrosine Kinase Interaction
4.2.2.6 G Protein-Coupled Receptor Interaction
4.2.3 Factors Affecting Drug–Receptor Interactions
4.3 In Silico Methods for Modeling Drug–Receptor Interactions
4.3.1 Molecular Docking
4.3.2 Molecular Dynamics Simulations
4.3.3 Quantitative Structure–Activity Relationship Modeling
4.3.4 Pharmacophore Modeling
4.3.5 Interaction Fingerprints
4.3.5.1 Structural Interaction Fingerprint (SIFt)
4.3.5.2 ProLIF
4.3.5.3 Geometric Deep Learning
4.3.5.4 Machine Learning
4.3.6 Receptor-Guided Alignment
4.4 Applications of In Silico Modeling in Neuropharmacology
4.5 Case Studies
4.5.1 Virtual Screening for Anti-Alzheimer’s Drugs
4.5.2 Designing Antagonists of Dopamine D3 Receptor for Addiction Treatment
4.5.3 Structure-Based Design of Selective Serotonin Reuptake Inhibitors
4.5.4 Prediction of Blood–Brain Barrier Permeability
4.5.5 Design of Allosteric Modulators for G Protein-Coupled Receptors
4.6 Conclusion
References
5. Computational Attitudes in Counselling Psychology
Bharat Mishra, Farha Deeba Khan, Archita Tiwari and Anitta Joseph
List of Abbreviations
5.1 Introduction
5.1.1 Counselling Psychology
5.1.2 Indications for Psychological Therapy
5.1.2.1 Types of Psychological Therapies
5.1.3 Why Do We Need Computer Models in the Field of Psychology?
5.1.4 Limitations of Methodologies in Psychological Therapy
5.1.5 Evolution of Technology in Counselling Psychology
5.1.6 Emergence of Computational Attitudes in Counselling Psychology
5.1.7 Current Trends and Challenges
5.2 Theoretical Foundations of Computational Attitude
5.2.1 Cognitive and Behavioral Theories in Computational Counselling
5.2.1.1 Cognitive Theories
5.2.1.2 Behavioral Theories
5.2.1.3 Integrative Approaches
5.2.2 Key Technologies in Computational Counselling
5.2.2.1 Artificial Intelligence and Natural Language Processing
5.3 Empirical Evidence and Efficacy of Computational Counselling
5.3.1 Case Studies: Exemplifying Computational Attitudes in Practice
5.3.2 Case Study: AI-Driven Virtual Therapist for Anxiety Management
5.3.3 Case Study: Virtual Reality Exposure Therapy (VRET) for Specific Phobia
5.3.4 Case Study: Discriminating Between Genuine and Faked Expressions of Pain in the Lab
5.4 Ethical and Legal Considerations
5.5 Future Directions and Possibilities
5.6 Conclusion
References
6. Computational Psychiatry: Addressing the Gap Between Pathophysiology and Psychopathology
Jignasha Derasari Pandya and Bhupendra Prajapati
List of Abbreviations
6.1 Introduction
6.1.1 Psychiatry
6.1.1.1 Clinical Challenges
6.1.1.2 Clinical Load and Diagnostic Complication
6.1.1.3 Treatment Development
6.2 Roadmap of Conventional to Modern Evolution Towards Mental (Psychological) Illness
6.3 Pathophysiology of Mental Illness
6.3.1 Overview of Mental Illness and Pathophysiology
6.3.1.1 Signs and Symptoms
6.3.1.2 Pathophysiology
6.3.2 Evolving Background and Scientific Progress
6.3.3 Limitation of Current Approaches
6.3.4 Basis for Discovery of Coherent Psychopathology
6.4 Psychopathology
6.4.1 Advent of Various Approaches to Psychopathology
6.4.2 Diagnostic Models of Psychopathology
6.4.2.1 Traditional Diagnostic Approaches
6.4.2.2 Dimensional Models of Psychopathology
6.4.3 Comparative Outlook
6.4.4 Major Psychological Disorders
6.4.4.1 Schizophrenia
6.4.4.2 Mood Disorders
6.4.4.3 Anxiety Disorders
6.4.4.4 Eating Disorders
6.4.5 Concluding Observations of Psychological Disorders
6.5 Computational Psychiatry (CP)
6.5.1 Advent of Computational Psychiatry: Reshaping the Existing Nosology of Mental Illness
6.5.2 Computational Psychiatry: Important Levels and Incorporated Models in Different Approaches
6.5.2.1 Important Computational Stages
6.5.2.2 Computational Approaches
6.5.3 Obstacles and Resolutions to Implementation of Computational Psychiatry in Real World
6.5.4 Applications Area Where Computational Psychiatry Might Be Useful
6.5.5 Challenges Cum Limitations of Computational Psychiatry
6.6 Computational Psychiatry: An Advanced Version Links Pathology and Psychopathology
6.6.1 Link to Neuroscience/Pathophysiology
6.6.2 Link to Traditional Psychiatry/Psychopathology
6.6.3 Link to Computation
6.6.4 CP Addresses the Gap Between Pathophysiology and Psychopathology
6.7 Conclusion
References
7. Computational Neuropharmacology in Psychiatry
Amol D. Gholap, Pankaj R. Khuspe, Deepak K. Bharati, Sagar R. Pardeshi, Mohammad Dabeer Ahmad, ABM Sharif Hossain, Bhupendra G. Prajapati and Md. Faiyazuddin
List of Abbreviations
7.1 Introduction
7.2 Need for Computational Neuropharmacology in Psychiatry
7.3 Data-Driven Computational Approaches in Psychiatry
7.4 Role of Diagnostic Classification
7.5 Machine Learning and Diagnostic Precision
7.6 The Challenges of Treatment Response Prediction
7.7 Future Implications and Ethical Considerations
7.8 Machine Learning for Informed Decisions
7.9 Network Analysis: Unraveling Symptom Dynamics
7.10 Theory-Driven Computational Approaches: Integrating Knowledge and Data
7.11 Biophysically Realistic Neural Network Models: Bridging the Gap Between Biology and Computation
7.12 Bayesian Models
7.13 Combining Data-Driven and Theory-Driven Computational Approaches
7.14 Conclusion
References
Part 2: Clinical Aspects of Computational Neuropharmacology
8. Computational Attitudes to Drug Discovery in Neurohumoral Transmission and Signal Transduction
Lucy Mohapatra, Alok S. Tripathi, Deepak Mishra, Alka, Sambit Kumar Parida and Bhupendra Gopalbhai Prajapati
Abbreviations
8.1 Introduction
8.2 Neurohumoral Transmission and Signal Transduction
8.2.1 Basic Steps Involved in Neurohumoral Transmission
8.2.1.1 Impulse Conduction
8.2.1.2 Transmitter Release
8.2.1.3 Transmitter Action on Postjunctional Membrane
8.2.1.4 Postjunctional Activity
8.2.1.5 Termination of Transmitter Action
8.2.2 Excitatory and Inhibitory Neurotransmitter
8.2.3 Excitatory and Inhibitory Neurotransmitters in Various Pathological Conditions
8.2.4 Cotransmission
8.3 Computational Approach in Creating Neurohumoral and Synaptic Models
8.3.1 Mechanisms and Models of Synaptic Transmission
8.3.1.1 Electrical Synapses
8.3.1.2 Chemical Synapses
8.3.1.3 The Granular Layer
8.3.1.4 The Molecular Layer
8.4 Primitive Computational Models
8.4.1 Metabotropic Receptor
8.4.2 Electrical Synapses and Ephaptic Coupling
8.5 Conclusion
References
9. Computational Attitude to Drug Discovery in Parkinson’s Disease
Chitra Vellapandian, Ankul Singh S., Swathi Suresh and Bhupendra Prajapati
List of Abbreviations
9.1 Introduction
9.2 PD and Drug Development
9.3 Animal Models and Translational Discovery
9.4 Pathophysiology
9.5 Validated Biomarkers
9.6 Computational Drug Discovery
9.6.1 Screening of Active Genes
9.6.2 Protein-Protein Interaction (PPI) Network Establishment
9.6.3 Gene Ontology Functional Enrichment and the Kyoto Encyclopedia of Genes and Genomes Pathway Analysis
9.7 Outcomes From Gene Ontology and KEGG Analysis
9.7.1 PD-Related Target Screening Results
9.7.2 PPI Network Construction
9.7.3 Protein–Protein Interaction Enrichment Analysis
9.7.4 GO Functional Enrichment and KEGG Pathway Analysis
9.7.5 Molecular Function
9.7.6 Cellular Component Analysis
9.7.7 KEGG Analysis
9.8 Conclusion
Acknowledgments
References
10. Computational Attitudes to Drug Discovery in Epilepsy
Shama Mujawar, Aarohi Deshpande, Avni Bhambure, Shreyash Kolhe and Bhupendra Prajapati
List of Abbreviations
10.1 Introduction
10.2 Traditional Drug Discovery Approaches for Epilepsy
10.2.1 Animal Models
10.2.2 High Throughput Screening
10.2.3 Computational Approaches in Epilepsy Drug Discovery
10.2.4 Computational Methods Applied to Enhance Drug Discovery Processes in Epilepsy
10.2.5 Identifying Potential Antiepileptic Compounds for Drug Discovery
10.2.5.1 Molecular Docking
10.2.5.2 Virtual Screening
10.2.5.3 Pharmacophore Modeling
10.3 Computer Simulations in Understanding and Optimizing Drug Efficacy
10.4 Development of Computational Models
10.4.1 Models for Neural Networks
10.4.2 Seizure Models
10.4.3 Use of Computational Models of Brain Networks
10.4.3.1 Recognizing Epileptogenesis
10.4.3.2 The Creation of Antiepileptic Medications
10.4.4 Planning for Epilepsy Surgery
10.4.5 Connectivity and Network Topology
10.4.6 Seizure Initiation and Propagation
10.4.7 Predictive Modeling and Personalized Medicine
10.5 Computational Models for Predicting Effects on Seizure Activity
10.5.1 Pharmacophore Modeling
10.5.2 Systems Pharmacology Methods
10.5.3 Implantable Devices
10.6 Data Integration and Analysis in Epilepsy Research
10.6.1 Epilepsy Research: Challenges in Integrating and Analyzing Diverse Data Types
10.6.1.1 Genomics Data
10.6.1.2 Proteomics Data
10.6.1.3 EEG Data
10.6.2 Role of Computational Techniques in Extracting Insights From Large-Scale Datasets
10.6.2.1 Data Mining
10.6.2.2 Machine Learning
10.6.2.3 Network Analysis
10.6.3 Identification of New Targets or Biomarkers for the Development of Epilepsy Drugs
10.6.3.1 Targeted Pathways Identification
10.6.3.2 Biomarker Discovery for Treatment Responses
10.6.3.3 Biomarker Development for Seizure Prediction
10.7 Challenges and Future Directions
10.8 Conclusion
Acknowledgments
References
11. Computational Attitudes to Drug Discovery in Alzheimer’s Disease
Shubhrat Maheshwari, Aditya Singh, Amita Verma, Juber Akhtar, Jigna B. Prajapati, Sudarshan Singh and Bhupendra Prajapati
List of Abbreviations
11.1 Introduction
11.2 Alzheimer’s Disease
11.3 Computational Attitudes to Drug Discovery
11.4 Applications of Computational Attitudes to Drug Development Process
11.5 Conclusion
References
12. The Integration of Molecular Docking and Machine Learning in Drug Discovery for Neurological Disorders
Aditya Singh, Shubhrat Maheshwari, Jigna B. Prajapati, Juber Akhtar, Syed Misbahul Hasan, Amita Verma, Sudarshan Singh and Bhupendra Prajapati
Abbreviations
12.1 Introduction
12.2 Neurodegenerative Disease
12.3 Molecular Docking
12.3.1 Pharmacophore Modeling
12.3.2 QSAR
12.3.3 Homology Modeling
12.4 Machine Learning in Drug Discovery
12.5 Random Forest
12.6 Naïve Bayesian
12.7 Support Vector Machine
12.8 Conclusion
References
13. Computational Attitudes to Drug Discovery in Autism Spectrum Disorder
Himani Nautiyal, Shubham Dwivedi, Silpi Chanda and Raj Kumar Tiwari
List of Abbreviations
13.1 Introduction
13.1.1 Genetic Identification and Analysis of Sequences
13.1.2 Phylogenetic Analysis
13.1.3 Nucleotide Sequence Databases
13.1.4 Genome Sequence Databases
13.1.5 Protein Sequence Databases
13.1.6 Predicting Protein Structure and Function
13.1.7 Molecular Interactions
13.1.8 Drug Designing
13.1.9 Molecular Dynamic Simulations
13.2 Clinical, Genetic, and Molecular Heterogeneity in Autism Spectrum Disorder
13.2.1 Genetics of Autism Spectrum Disorder
13.2.2 Metabolomics
13.2.3 Transcriptomics
13.3 The Necessity of Drug Discovery
13.4 Computational Model for Drug Discovery
13.5 Importance of Multiomics and Endophenotyping-Based Methods Toward Precision Medicine
13.6 Network-Based Approach for Diseases/Drug Modeling
13.6.1 Knowledge-Driven Networks
13.6.2 Data-Driven Networks
13.6.3 Network Construction by Combining Knowledge-Based and Data-Based Study
13.7 Drug Repurposing Candidates for Treatment of ASD Using Bioinformatic Approaches
13.8 Conclusion and Future Prospective
Acknowledgment
References
14. Computational Approaches to Drug Discovery in Depression
Kalpesh Ramdas Patil, Aman B. Upaganlawar, Akhil A. Nagar and Kuldeep U. Bansod
List of Abbreviations
14.1 Introduction
14.2 Types of Depressive Disorders
14.3 Hypotheses and Pathways of Depression
14.3.1 Monoamine Hypothesis
14.3.2 Hypothalamic Pituitary Adrenal Axis
14.3.3 Neurotrophic Factor Hypothesis
14.3.4 Oxidative Stress
14.3.5 Cytokines
14.4 Receptors in Depression
14.4.1 Serotonin Receptor
14.4.2 Dopamine Receptor
14.4.3 Glutamate Receptor
14.4.4 Trace Amine-Associated Receptors
14.4.5 Cholinergic Receptor
14.4.6 GABA Receptor
14.4.7 Cannabinoid Receptor
14.5 Computational Approaches to Depression
14.6 Network Pharmacology of Depression
14.6.1 The Psycho-Immune Neuroendocrine (PINE) Network Model
14.7 Conclusion
References
15. Computational Attitudes to Drug Discovery in Anxiety
Meenakshi Attri, Asha Raghav, Piyush Vatsha, Mohit Agrawal, Manmohan Singhal, Hema Chaudhary, Nalini Kanta Sahoo and Bhupendra Prajapati
List of Abbreviations
15.1 Introduction
15.2 Computational Approaches for Drug Discovery
15.2.1 Virtual Screening (VS)
15.2.1.1 Methods of VS
15.3 Ligand-Based Techniques
15.3.1 Modeling Using (Quantitative Structure-Activity Relationship) QSAR
15.3.2 QSAR Used in Mood and Anxiety Disorder
15.4 Pharmacophore
15.4.1 Concept and Definition of Pharmacophore
15.4.2 Types of Pharmacophore
15.5 Structure-Based Methods for Screening
15.5.1 Protein–Ligand Docking Techniques Based on Structure
15.5.2 Molecular Docking
15.5.3 Autodocking
15.6 AI
15.7 Machine Learning Algorithms for Anxiety Disorder Detection and Prediction
15.8 A Review of the Literature on Machine Learning Approaches for Anxiety-Related Disorders
15.9 Molecular Dynamic Simulation
15.9.1 Network Analysis
15.9.2 Bioinformatics
15.9.3 Recent Advancementon Computation Approaches in Anxiety
15.10 Future Prospective
15.11 Conclusion
References
Index

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