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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.
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.
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.
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 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.
Foreword xix
Preface xxi
Part 1: Fundamentals of Computational Neuropharmacology 1
1 Basic Principles of Computational Neuropharmacology: Neuroscience Meeting Pharmacology 3Lucy Mohapatra, Alok S. Tripathi, Deepak Mishra, Alka and Sambit Kumar Parida
Abbreviations 4
1.1 Introduction 5
1.2 Basics of Computational Neuropharmacology 6
1.3 Multiple Aspects of Computational Neuropharmacology 11
1.4 Recent Developments in Computational Neuropharmacology 18
1.5 Limitations of Computational Neuropharmacology 21
1.6 Conclusion 22
References 22
2 Neuropharmacology in the Molecular Epoch 31Neelakanta Sarvashiva Kiran, Chandrashekar Yashaswini and Bhupendra G. Prajapati
List of Abbreviations 32
2.1 Introduction 33
2.2 History of Neuropharmacology 34
2.3 Neurochemical Interactions 35
2.4 Molecular Pharmacology of Neuronal Receptors 37
2.5 Neuropharmacological Drugs 46
2.6 Impact of Biotechnology of Neuropharmacology 50
2.7 Future Research and Perspectives 55
2.8 Conclusion 56
Acknowledgments 57
References 57
3 Basics of Theoretical Neuroscience 67Anil P. Dewani, Deepak S. Mohale, Alok S. Tripathi and Naheed Waseem A. Sheikh
List of Abbreviations 67
3.1 Introduction 68
3.2 Properties of Neurons and Neuronal Signaling 70
3.3 Recording Neuronal Responses 72
3.4 Neural Encoding and Neuronal Decoding 74
3.5 Neuronal Network Models 76
3.6 Learning and Synaptic Plasticity 78
3.7 Conclusion 79
References 80
4 In Silico Modeling of Drug-Receptor Interactions for Rational Drug Design in Neuropharmacology 87Princy Shrivastav, Bhupendra Prajapati, Chandni Chandarana and Parixit Prajapati
Abbreviations 88
4.1 Introduction 88
4.2 Drug-Receptor Interactions 93
4.3 In Silico Methods for Modeling Drug-Receptor Interactions 101
4.4 Applications of In Silico Modeling in Neuropharmacology 115
4.5 Case Studies 116
4.6 Conclusion 120
References 121
5 Computational Attitudes in Counselling Psychology 127Bharat Mishra, Farha Deeba Khan, Archita Tiwari and Anitta Joseph
List of Abbreviations 128
5.1 Introduction 129
5.2 Theoretical Foundations of Computational Attitude 139
5.3 Empirical Evidence and Efficacy of Computational Counselling 149
5.4 Ethical and Legal Considerations 153
5.5 Future Directions and Possibilities 153
5.6 Conclusion 154
References 154
6 Computational Psychiatry: Addressing the Gap Between Pathophysiology and Psychopathology 159Jignasha Derasari Pandya and Bhupendra Prajapati
List of Abbreviations 160
6.1 Introduction 160
6.2 Roadmap of Conventional to Modern Evolution Towards Mental (Psychological) Illness 165
6.3 Pathophysiology of Mental Illness 167
6.4 Psychopathology 174
6.5 Computational Psychiatry (CP) 182
6.6 Computational Psychiatry: An Advanced Version Links Pathology and Psychopathology 191
6.7 Conclusion 193
References 193
7 Computational Neuropharmacology in Psychiatry 207Amol 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 208
7.1 Introduction 208
7.2 Need for Computational Neuropharmacology in Psychiatry 209
7.3 Data-Driven Computational Approaches in Psychiatry 211
7.4 Role of Diagnostic Classification 212
7.5 Machine Learning and Diagnostic Precision 213
7.6 The Challenges of Treatment Response Prediction 214
7.7 Future Implications and Ethical Considerations 216
7.8 Machine Learning for Informed Decisions 217
7.9 Network Analysis: Unraveling Symptom Dynamics 218
7.10 Theory-Driven Computational Approaches: Integrating Knowledge and Data 221
7.11 Biophysically Realistic Neural Network Models: Bridging the Gap Between Biology and Computation 222
7.12 Bayesian Models 225
7.13 Combining Data-Driven and Theory-Driven Computational Approaches 226
7.14 Conclusion 228
References 229
Part 2: Clinical Aspects of Computational Neuropharmacology 245
8 Computational Attitudes to Drug Discovery in Neurohumoral Transmission and Signal Transduction 247Lucy Mohapatra, Alok S. Tripathi, Deepak Mishra, Alka, Sambit Kumar Parida and Bhupendra Gopalbhai Prajapati
Abbreviations 248
8.1 Introduction 248
8.2 Neurohumoral Transmission and Signal Transduction 250
8.3 Computational Approach in Creating Neurohumoral and Synaptic Models 257
8.4 Primitive Computational Models 261
8.5 Conclusion 263
References 264
9 Computational Attitude to Drug Discovery in Parkinson's Disease 271Chitra Vellapandian, Ankul Singh S., Swathi Suresh and Bhupendra Prajapati
List of Abbreviations 272
9.1 Introduction 273
9.2 PD and Drug Development 275
9.3 Animal Models and Translational Discovery 276
9.4 Pathophysiology 278
9.5 Validated Biomarkers 279
9.6 Computational Drug Discovery 282
9.7 Outcomes From Gene Ontology and KEGG Analysis 284
9.8 Conclusion 299
Acknowledgments 300
References 300
10 Computational Attitudes to Drug Discovery in Epilepsy 313Shama Mujawar, Aarohi Deshpande, Avni Bhambure, Shreyash Kolhe and Bhupendra Prajapati
List of Abbreviations 314
10.1 Introduction 314
10.2 Traditional Drug Discovery Approaches for Epilepsy 315
10.3 Computer Simulations in Understanding and Optimizing Drug Efficacy 319
10.4 Development of Computational Models 321
10.5 Computational Models for Predicting Effects on Seizure Activity 323
10.6 Data Integration and Analysis in Epilepsy Research 325
10.7 Challenges and Future Directions 328
10.8 Conclusion 330
Acknowledgments 331
References 331
11 Computational Attitudes to Drug Discovery in Alzheimer's Disease 335Shubhrat Maheshwari, Aditya Singh, Amita Verma, Juber Akhtar, Jigna B. Prajapati, Sudarshan Singh and Bhupendra Prajapati
List of Abbreviations 336
11.1 Introduction 336
11.2 Alzheimer's Disease 339
11.3 Computational Attitudes to Drug Discovery 341
11.4 Applications of Computational Attitudes to Drug Development Process 343
11.5 Conclusion 345
References 345
12 The Integration of Molecular Docking and Machine Learning in Drug Discovery for Neurological Disorders 349Aditya Singh, Shubhrat Maheshwari, Jigna B. Prajapati, Juber Akhtar, Syed Misbahul Hasan, Amita Verma, Sudarshan Singh and Bhupendra Prajapati
Abbreviations 350
12.1 Introduction 351
12.2 Neurodegenerative Disease 355
12.3 Molecular Docking 357
12.4 Machine Learning in Drug Discovery 361
12.5 Random Forest 366
12.6 Naïve Bayesian 366
12.7 Support Vector Machine 367
12.8 Conclusion 368
References 369
13 Computational Attitudes to Drug Discovery in Autism Spectrum Disorder 375Himani Nautiyal, Shubham Dwivedi, Silpi Chanda and Raj Kumar Tiwari
List of Abbreviations 376
13.1 Introduction 376
13.2 Clinical, Genetic, and Molecular Heterogeneity in Autism Spectrum Disorder 387
13.3 The Necessity of Drug Discovery 390
13.4 Computational Model for Drug Discovery 391
13.5 Importance of Multiomics and Endophenotyping-Based Methods Toward Precision Medicine 392
13.6 Network-Based Approach for Diseases/Drug Modeling 393
13.7 Drug Repurposing Candidates for Treatment of ASD Using Bioinformatic Approaches 395
13.8 Conclusion and Future Prospective 398
Acknowledgment 398
References 399
14 Computational Approaches to Drug Discovery in Depression 409Kalpesh Ramdas Patil, Aman B. Upaganlawar, Akhil A. Nagar and Kuldeep U. Bansod
List of Abbreviations 410
14.1 Introduction 411
14.2 Types of Depressive Disorders 411
14.3 Hypotheses and Pathways of Depression 412
14.4 Receptors in Depression 415
14.5 Computational Approaches to Depression 417
14.6 Network Pharmacology of Depression 426
14.7 Conclusion 429
References 429
15 Computational Attitudes to Drug Discovery in Anxiety 437Meenakshi Attri, Asha Raghav, Piyush Vatsha, Mohit Agrawal, Manmohan Singhal, Hema Chaudhary, Nalini Kanta Sahoo and Bhupendra Prajapati
List of Abbreviations 438
15.1 Introduction 439
15.2 Computational Approaches for Drug Discovery 439
15.3 Ligand-Based Techniques 443
15.4 Pharmacophore 444
15.5 Structure-Based Methods for Screening 447
15.6 Ai 449
15.7 Machine Learning Algorithms for Anxiety Disorder Detection and Prediction 450
15.8 A Review of the Literature on Machine Learning Approaches for Anxiety-Related Disorders 453
15.9 Molecular Dynamic Simulation 454
15.10 Future Prospective 460
15.11 Conclusion 470
References 471
Index 483
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