{"product_id":"computational-neuropharmacology-fundamentals-and-clinical-aspects-hardback-9781394242443","title":"Computational Neuropharmacology; Fundamentals and Clinical Aspects (Hardback) 9781394242443","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eComputational Neuropharmacology\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFundamentals and Clinical Aspects\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eBhupendra Prajapati (Edited by), Prajapati (Author), Alok Tripathi (Edited by), Rishabha Malviya (Edited by), Lucy Mohapatra (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394242443, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 9 May 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages\u003cbr\u003e28 x 19 x 3 cm, 0.666 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eComputational Neuropharmacology\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eThis 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. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThis 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Fundamentals of Computational Neuropharmacology 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Basic Principles of Computational Neuropharmacology: Neuroscience Meeting Pharmacology 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLucy Mohapatra, Alok S. Tripathi, Deepak Mishra, Alka and Sambit Kumar Parida\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 4\u003c\/p\u003e \u003cp\u003e1.1 Introduction 5\u003c\/p\u003e \u003cp\u003e1.2 Basics of Computational Neuropharmacology 6\u003c\/p\u003e \u003cp\u003e1.3 Multiple Aspects of Computational Neuropharmacology 11\u003c\/p\u003e \u003cp\u003e1.4 Recent Developments in Computational Neuropharmacology 18\u003c\/p\u003e \u003cp\u003e1.5 Limitations of Computational Neuropharmacology 21\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 22\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Neuropharmacology in the Molecular Epoch 31\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNeelakanta Sarvashiva Kiran, Chandrashekar Yashaswini and Bhupendra G. Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 32\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 History of Neuropharmacology 34\u003c\/p\u003e \u003cp\u003e2.3 Neurochemical Interactions 35\u003c\/p\u003e \u003cp\u003e2.4 Molecular Pharmacology of Neuronal Receptors 37\u003c\/p\u003e \u003cp\u003e2.5 Neuropharmacological Drugs 46\u003c\/p\u003e \u003cp\u003e2.6 Impact of Biotechnology of Neuropharmacology 50\u003c\/p\u003e \u003cp\u003e2.7 Future Research and Perspectives 55\u003c\/p\u003e \u003cp\u003e2.8 Conclusion 56\u003c\/p\u003e \u003cp\u003eAcknowledgments 57\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Basics of Theoretical Neuroscience 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnil P. Dewani, Deepak S. Mohale, Alok S. Tripathi and Naheed Waseem A. Sheikh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 67\u003c\/p\u003e \u003cp\u003e3.1 Introduction 68\u003c\/p\u003e \u003cp\u003e3.2 Properties of Neurons and Neuronal Signaling 70\u003c\/p\u003e \u003cp\u003e3.3 Recording Neuronal Responses 72\u003c\/p\u003e \u003cp\u003e3.4 Neural Encoding and Neuronal Decoding 74\u003c\/p\u003e \u003cp\u003e3.5 Neuronal Network Models 76\u003c\/p\u003e \u003cp\u003e3.6 Learning and Synaptic Plasticity 78\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 79\u003c\/p\u003e \u003cp\u003eReferences 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 In Silico Modeling of Drug–Receptor Interactions for Rational Drug Design in Neuropharmacology 87\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrincy Shrivastav, Bhupendra Prajapati, Chandni Chandarana and Parixit Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 88\u003c\/p\u003e \u003cp\u003e4.1 Introduction 88\u003c\/p\u003e \u003cp\u003e4.2 Drug–Receptor Interactions 93\u003c\/p\u003e \u003cp\u003e4.3 In Silico Methods for Modeling Drug–Receptor Interactions 101\u003c\/p\u003e \u003cp\u003e4.4 Applications of In Silico Modeling in Neuropharmacology 115\u003c\/p\u003e \u003cp\u003e4.5 Case Studies 116\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Computational Attitudes in Counselling Psychology 127\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBharat Mishra, Farha Deeba Khan, Archita Tiwari and Anitta Joseph\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 128\u003c\/p\u003e \u003cp\u003e5.1 Introduction 129\u003c\/p\u003e \u003cp\u003e5.2 Theoretical Foundations of Computational Attitude 139\u003c\/p\u003e \u003cp\u003e5.3 Empirical Evidence and Efficacy of Computational Counselling 149\u003c\/p\u003e \u003cp\u003e5.4 Ethical and Legal Considerations 153\u003c\/p\u003e \u003cp\u003e5.5 Future Directions and Possibilities 153\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 154\u003c\/p\u003e \u003cp\u003eReferences 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Computational Psychiatry: Addressing the Gap Between Pathophysiology and Psychopathology 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJignasha Derasari Pandya and Bhupendra Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 160\u003c\/p\u003e \u003cp\u003e6.1 Introduction 160\u003c\/p\u003e \u003cp\u003e6.2 Roadmap of Conventional to Modern Evolution Towards Mental (Psychological) Illness 165\u003c\/p\u003e \u003cp\u003e6.3 Pathophysiology of Mental Illness 167\u003c\/p\u003e \u003cp\u003e6.4 Psychopathology 174\u003c\/p\u003e \u003cp\u003e6.5 Computational Psychiatry (CP) 182\u003c\/p\u003e \u003cp\u003e6.6 Computational Psychiatry: An Advanced Version Links Pathology and Psychopathology 191\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 193\u003c\/p\u003e \u003cp\u003eReferences 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Computational Neuropharmacology in Psychiatry 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmol D. Gholap, Pankaj R. Khuspe, Deepak K. Bharati, Sagar R. Pardeshi, Mohammad Dabeer Ahmad, ABM Sharif Hossain, Bhupendra G. Prajapati and Md. Faiyazuddin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 208\u003c\/p\u003e \u003cp\u003e7.1 Introduction 208\u003c\/p\u003e \u003cp\u003e7.2 Need for Computational Neuropharmacology in Psychiatry 209\u003c\/p\u003e \u003cp\u003e7.3 Data-Driven Computational Approaches in Psychiatry 211\u003c\/p\u003e \u003cp\u003e7.4 Role of Diagnostic Classification 212\u003c\/p\u003e \u003cp\u003e7.5 Machine Learning and Diagnostic Precision 213\u003c\/p\u003e \u003cp\u003e7.6 The Challenges of Treatment Response Prediction 214\u003c\/p\u003e \u003cp\u003e7.7 Future Implications and Ethical Considerations 216\u003c\/p\u003e \u003cp\u003e7.8 Machine Learning for Informed Decisions 217\u003c\/p\u003e \u003cp\u003e7.9 Network Analysis: Unraveling Symptom Dynamics 218\u003c\/p\u003e \u003cp\u003e7.10 Theory-Driven Computational Approaches: Integrating Knowledge and Data 221\u003c\/p\u003e \u003cp\u003e7.11 Biophysically Realistic Neural Network Models: Bridging the Gap Between Biology and Computation 222\u003c\/p\u003e \u003cp\u003e7.12 Bayesian Models 225\u003c\/p\u003e \u003cp\u003e7.13 Combining Data-Driven and Theory-Driven Computational Approaches 226\u003c\/p\u003e \u003cp\u003e7.14 Conclusion 228\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Clinical Aspects of Computational Neuropharmacology 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Computational Attitudes to Drug Discovery in Neurohumoral Transmission and Signal Transduction 247\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLucy Mohapatra, Alok S. Tripathi, Deepak Mishra, Alka, Sambit Kumar Parida and Bhupendra Gopalbhai Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 248\u003c\/p\u003e \u003cp\u003e8.1 Introduction 248\u003c\/p\u003e \u003cp\u003e8.2 Neurohumoral Transmission and Signal Transduction 250\u003c\/p\u003e \u003cp\u003e8.3 Computational Approach in Creating Neurohumoral and Synaptic Models 257\u003c\/p\u003e \u003cp\u003e8.4 Primitive Computational Models 261\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 263\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Computational Attitude to Drug Discovery in Parkinson’s Disease 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChitra Vellapandian, Ankul Singh S., Swathi Suresh and Bhupendra Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 272\u003c\/p\u003e \u003cp\u003e9.1 Introduction 273\u003c\/p\u003e \u003cp\u003e9.2 PD and Drug Development 275\u003c\/p\u003e \u003cp\u003e9.3 Animal Models and Translational Discovery 276\u003c\/p\u003e \u003cp\u003e9.4 Pathophysiology 278\u003c\/p\u003e \u003cp\u003e9.5 Validated Biomarkers 279\u003c\/p\u003e \u003cp\u003e9.6 Computational Drug Discovery 282\u003c\/p\u003e \u003cp\u003e9.7 Outcomes From Gene Ontology and KEGG Analysis 284\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 299\u003c\/p\u003e \u003cp\u003eAcknowledgments 300\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Computational Attitudes to Drug Discovery in Epilepsy 313\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShama Mujawar, Aarohi Deshpande, Avni Bhambure, Shreyash Kolhe and Bhupendra Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 314\u003c\/p\u003e \u003cp\u003e10.1 Introduction 314\u003c\/p\u003e \u003cp\u003e10.2 Traditional Drug Discovery Approaches for Epilepsy 315\u003c\/p\u003e \u003cp\u003e10.3 Computer Simulations in Understanding and Optimizing Drug Efficacy 319\u003c\/p\u003e \u003cp\u003e10.4 Development of Computational Models 321\u003c\/p\u003e \u003cp\u003e10.5 Computational Models for Predicting Effects on Seizure Activity 323\u003c\/p\u003e \u003cp\u003e10.6 Data Integration and Analysis in Epilepsy Research 325\u003c\/p\u003e \u003cp\u003e10.7 Challenges and Future Directions 328\u003c\/p\u003e \u003cp\u003e10.8 Conclusion 330\u003c\/p\u003e \u003cp\u003eAcknowledgments 331\u003c\/p\u003e \u003cp\u003eReferences 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Computational Attitudes to Drug Discovery in Alzheimer’s Disease 335\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShubhrat Maheshwari, Aditya Singh, Amita Verma, Juber Akhtar, Jigna B. Prajapati, Sudarshan Singh and Bhupendra Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 336\u003c\/p\u003e \u003cp\u003e11.1 Introduction 336\u003c\/p\u003e \u003cp\u003e11.2 Alzheimer’s Disease 339\u003c\/p\u003e \u003cp\u003e11.3 Computational Attitudes to Drug Discovery 341\u003c\/p\u003e \u003cp\u003e11.4 Applications of Computational Attitudes to Drug Development Process 343\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 345\u003c\/p\u003e \u003cp\u003eReferences 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Integration of Molecular Docking and Machine Learning in Drug Discovery for Neurological Disorders 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAditya Singh, Shubhrat Maheshwari, Jigna B. Prajapati, Juber Akhtar, Syed Misbahul Hasan, Amita Verma, Sudarshan Singh and Bhupendra Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 350\u003c\/p\u003e \u003cp\u003e12.1 Introduction 351\u003c\/p\u003e \u003cp\u003e12.2 Neurodegenerative Disease 355\u003c\/p\u003e \u003cp\u003e12.3 Molecular Docking 357\u003c\/p\u003e \u003cp\u003e12.4 Machine Learning in Drug Discovery 361\u003c\/p\u003e \u003cp\u003e12.5 Random Forest 366\u003c\/p\u003e \u003cp\u003e12.6 Naïve Bayesian 366\u003c\/p\u003e \u003cp\u003e12.7 Support Vector Machine 367\u003c\/p\u003e \u003cp\u003e12.8 Conclusion 368\u003c\/p\u003e \u003cp\u003eReferences 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Computational Attitudes to Drug Discovery in Autism Spectrum Disorder 375\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHimani Nautiyal, Shubham Dwivedi, Silpi Chanda and Raj Kumar Tiwari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 376\u003c\/p\u003e \u003cp\u003e13.1 Introduction 376\u003c\/p\u003e \u003cp\u003e13.2 Clinical, Genetic, and Molecular Heterogeneity in Autism Spectrum Disorder 387\u003c\/p\u003e \u003cp\u003e13.3 The Necessity of Drug Discovery 390\u003c\/p\u003e \u003cp\u003e13.4 Computational Model for Drug Discovery 391\u003c\/p\u003e \u003cp\u003e13.5 Importance of Multiomics and Endophenotyping-Based Methods Toward Precision Medicine 392\u003c\/p\u003e \u003cp\u003e13.6 Network-Based Approach for Diseases\/Drug Modeling 393\u003c\/p\u003e \u003cp\u003e13.7 Drug Repurposing Candidates for Treatment of ASD Using Bioinformatic Approaches 395\u003c\/p\u003e \u003cp\u003e13.8 Conclusion and Future Prospective 398\u003c\/p\u003e \u003cp\u003eAcknowledgment 398\u003c\/p\u003e \u003cp\u003eReferences 399\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Computational Approaches to Drug Discovery in Depression 409\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKalpesh Ramdas Patil, Aman B. Upaganlawar, Akhil A. Nagar and Kuldeep U. Bansod\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 410\u003c\/p\u003e \u003cp\u003e14.1 Introduction 411\u003c\/p\u003e \u003cp\u003e14.2 Types of Depressive Disorders 411\u003c\/p\u003e \u003cp\u003e14.3 Hypotheses and Pathways of Depression 412\u003c\/p\u003e \u003cp\u003e14.4 Receptors in Depression 415\u003c\/p\u003e \u003cp\u003e14.5 Computational Approaches to Depression 417\u003c\/p\u003e \u003cp\u003e14.6 Network Pharmacology of Depression 426\u003c\/p\u003e \u003cp\u003e14.7 Conclusion 429\u003c\/p\u003e \u003cp\u003eReferences 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Computational Attitudes to Drug Discovery in Anxiety 437\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMeenakshi Attri, Asha Raghav, Piyush Vatsha, Mohit Agrawal, Manmohan Singhal, Hema Chaudhary, Nalini Kanta Sahoo and Bhupendra Prajapati\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eList of Abbreviations 438\u003c\/p\u003e \u003cp\u003e15.1 Introduction 439\u003c\/p\u003e \u003cp\u003e15.2 Computational Approaches for Drug Discovery 439\u003c\/p\u003e \u003cp\u003e15.3 Ligand-Based Techniques 443\u003c\/p\u003e \u003cp\u003e15.4 Pharmacophore 444\u003c\/p\u003e \u003cp\u003e15.5 Structure-Based Methods for Screening 447\u003c\/p\u003e \u003cp\u003e15.6 Ai 449\u003c\/p\u003e \u003cp\u003e15.7 Machine Learning Algorithms for Anxiety Disorder Detection and Prediction 450\u003c\/p\u003e \u003cp\u003e15.8 A Review of the Literature on Machine Learning Approaches for Anxiety-Related Disorders 453\u003c\/p\u003e \u003cp\u003e15.9 Molecular Dynamic Simulation 454\u003c\/p\u003e \u003cp\u003e15.10 Future Prospective 460\u003c\/p\u003e \u003cp\u003e15.11 Conclusion 470\u003c\/p\u003e \u003cp\u003eReferences 471\u003c\/p\u003e \u003cp\u003eIndex 483\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Chemistry [\u003ca title=\"See our other books on Chemistry\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Chemistry%20%5BPN%5D%22\"\u003ePN\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433232855320,"sku":"9781394242443","price":136.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394242443.jpg?v=1784852351","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/computational-neuropharmacology-fundamentals-and-clinical-aspects-hardback-9781394242443","provider":"Freshly Printed Books","version":"1.0","type":"link"}