Vivek Chavda, M. Pharm, is an assistant professor in the Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad, India. He has more than 40 research articles in international journals.

Krishnan Anand, PhD, is a research scientist in the Department of Chemical Pathology, University of the Free State, Bloemfontein, South Africa. He has more than 40 research articles in international journals and his research interests are in organic chemistry, medicinal chemistry, chemical pathology, bioinformatics, and nanotechnology.

Vasso Apostolopoulos, PhD, is at the Institute for Health and Sport, Immunology and Translational Research Group, Victoria University, Melbourne, Australia. She received her PhD in immunology in 1995 from the University of Melbourne, and the Advanced Certificate in Protein Crystallography from Birkbeck College, University of London. Professor Vasso Apostolopoulos is a world-renowned researcher who has been recognized with over 100 awards for the outstanding results of her research and she was named one of the most successful Greeks abroad by the prestigious Times magazine. Vasso was the first in the world to develop the concept of immunotherapy for cancer in the early 1990s, which today is used by hundreds of labs around the world. Immunotherapy aims to boost specific immune cells and program them to kill cancer cells; it was used by Vasso to develop the world's first breast cancer vaccine with phase I, II, and III clinical trials completed. Of note, one of the studies now has long-term follow-up data showing that 20 years later those injected with the vaccine remain cancer free.

Preface xv Part I: Bioinformatics Tools 1 1 Introduction to Bioinformatics, AI, and ml for Pharmaceuticals 3
Vivek P. Chavda, Disha Vihol, Aayushi Patel, Elrashdy M. Redwan and Vladimir N. Uversky 1.1 Introduction 4 1.2 Bioinformatics 4 1.2.1 Limitations of Bioinformatics 8 1.2.2 Artificial Intelligence (AI) 8 1.3 Machine Learning (ML) 11 1.3.1 Applications of ml 12 1.3.2 Limitations of ml 14 1.4 Conclusion and Future Prospects 14 References 15 2 Artificial Intelligence and Machine Learning-Based New Drug Discovery Process with Molecular Modelling 19Isha Rani, Kavita Munjal, Rajeev K. Singla and Rupesh K. Gautam 2.1 Introduction 20 2.2 Artificial Intelligence in Drug Discovery 21 2.2.1 Training Dataset Used in Medicinal Chemistry 22 2.2.2 Availability and Quality of Initial Data 23 2.3 AI in Virtual Screening 24 2.4 AI for De Novo Design 25 2.5 AI for Synthesis Planning 26 2.6 AI in Quality Control and Quality Assurance 27 2.7 AI-Based Advanced Applications 28 2.7.1 Micro/Nanorobot Targeted Drug Delivery System 28 2.7.2 AI in Nanomedicine 29 2.7.3 Role of AI in Market Prediction 29 2.8 Discussion and Future Perspectives 30 2.9 Conclusion 31 References 31 3 Role of Bioinformatics in Peptide-Based Drug Design and Its Serum Stability 37Vivek Chavda, Prashant Kshirsagar and Nildip Chauhan 3.1 Introduction 37 3.2 Points to be considered for Peptide-Based Delivery 38 3.3 Overview of Peptide-Based Drug Delivery System 40 3.4 Tools for Screening of Peptide Drug Candidate 41 3.5 Various Strategies to Increase Serum Stability of Peptide 42 3.5.1 Cyclization of Peptide 42 3.5.2 Incorporation of D Form of Amino Acid 44 3.5.3 Terminal Modification 44 3.5.4 Substitution of Amino Acid Which is Not Natural 46 3.5.5 Stapled Peptides 46 3.5.6 Synthesis of Stapled Peptides 47 3.6 Method/Tools for Serum Stability Evaluation 47 3.7 Conclusion 48 3.8 Future Prospects 49 References 49 4 Data Analytics and Data Visualization for the Pharmaceutical Industry 55Shalin Parikh, Ravi Patel, Dignesh Khunt, Vivek P. Chavda and Lalitkumar Vora 4.1 Introduction 56 4.2 Data Analytics 57 4.3 Data Visualization 58 4.4 Data Analytics and Data Visualization for Formulation Development 60 4.5 Data Analytics and Data Visualization for Drug Product Development 65 4.6 Data Analytics and Data Visualization for Drug Product Life Cycle Management 69 4.7 Conclusion and Future Prospects 71 References 72 5 Mass Spectrometry, Protein Interaction and Amalgamation of Bioinformatics 77Vivek Chavda, Kaustubh Dange and Madhav Joglekar 5.1 Introduction 77 5.2 Mass Spectrometry - Protein Interaction 79 5.2.1 The Prerequisites 80 5.2.2 Finding Affinity Partner (The Bait) 80 5.2.3 Antibody-Based Affinity Tags 80 5.2.4 Small Molecule Ligands 80 5.2.5 Fusion Protein-Based Affinity Tags 81 5.3 MS Analysis 81 5.4 Validating Specific Interactions 82 5.5 Mass Spectrometry - Qualitative and Quantitative Analysis 83 5.6 Challenges Associated with Mass Analysis 83 5.7 Relative vs. Absolute Quantification 85 5.8 Mass Spectrometry - Lipidomics and Metabolomics 86 5.9 Mass Spectrometry - Drug Discovery 87 5.10 Conclusion and Future Scope 88 5.11 Resources and Software 89 Acknowledgement 89 References 89 6 Applications of Bioinformatics Tools in Medicinal Biology and Biotechnology 95Harshil Shah, Vivek Chavda and Moinuddin M. Soniwala 6.1 Introduction 96 6.2 Bioinformatics Tools 97 6.3 The Genetic Basis of Diseases 97 6.4 Proteomics 98 6.5 Transcriptomic 100 6.6 Cancer 101 6.7 Diagnosis 102 6.8 Drug Discovery and Testing 103 6.9 Molecular Medicines 105 6.10 Personalized (Precision) Medicines 106 6.11 Vaccine Development and Drug Discovery in Infectious Diseases and COVID-19 Pandemic 108 6.12 Prognosis of Ailments 109 6.13 Concluding Remarks and Future Prospects 110 Acknowledgement 111 References 111 7 Clinical Applications of "Omics" Technology as a Bioinformatic Tool 117Vivek Chavda, Rajashri Bezbaruah, Disha Valu, Sanjay Desai, Nildip Chauhan, Swati Marwadi, Gitima Deka and Zhiyong Ding Abbreviations 118 7.1 Introduction 118 7.2 Execution Method 119 7.3 Overview of Omics Technology 121 7.4 Genomics 124 7.5 Nutrigenomics 127 7.6 Transcriptomics 128 7.7 Proteomics 129 7.8 Metabolomics 130 7.9 Lipomics or Lipidomics 133 7.10 Ayurgenomics 134 7.11 Pharmacogenomics 134 7.12 Toxicogenomic 135 7.13 Conclusion and Future Prospects 139 Acknowledgement 139 References 139 Part II: Bioinformatics Tools for Pharmaceutical Sector 147 8 Bioinformatics and Cheminformatics Tools in Early Drug Discovery 149Palak K. Parikh, Jignasa K. Savjani, Anuradha K. Gajjar and Mahesh T. Chhabria Abbreviations 150 8.1 Introduction 151 8.2 Informatics and Drug Discovery 152 8.3 Computational Methods in Drug Discovery 153 8.3.1 Homology Modeling 153 8.3.2 Docking Studies 155 8.3.3 Molecular Dynamics Simulations 158 8.3.4 De Novo Drug Design 159 8.3.5 Quantitative Structure Activity Relationships 160 8.3.6 Pharmacophore Modeling 161 8.3.7 Absorption, Distribution, Metabolism, Excretion and Toxicity Profiling 165 8.4 Conclusion 168 References 169 9 Artificial Intelligence and Machine Learning-Based Formulation and Process Development for Drug Products 183Vivek P. Chavda 9.1 Introduction 184 9.2 Current Scenario in Pharma Industry and Quality by Design (QbD) 185 9.3 AI- and ML-Based Formulation Development 187 9.4 AI- and ML-Based Process Development and Process Characterization 189 9.5 Concluding Remarks and Future Prospects 192 References 193 10 Artificial Intelligence and Machine Learning-Based Manufacturing and Drug Product Marketing 197Kajal Baviskar, Anjali Bedse, Shilpa Raut and Narayana Darapaneni Abbreviations 198 10.1 Introduction to Artificial Intelligence and Machine Learning 199 10.1.1 AI and ML in Pharmaceutical Manufacturing 200 10.1.2 AI and ML in Drug Product Marketing 201 10.2 Different Applications of AI and ML in the Pharma Field 202 10.2.1 Drug Discovery 202 10.2.2 Pharmaceutical Product Development 202 10.2.3 Clinical Trial Design 203 10.2.4 Manufacturing of Drugs 203 10.2.5 Quality Control and Quality Assurance 203 10.2.6 Product Management 203 10.2.7 Drug Prescription 204 10.2.8 Medical Diagnosis 204 10.2.9 Monitoring of Patients 204 10.2.10 Drug Synergism and Antagonism Prediction 204 10.2.11 Precision Medicine 205 10.3 AI and ML-Based Manufacturing 205 10.3.1 Continuous Manufacturing 205 10.3.2 Process Improvement and Fault Detection 209 10.3.3 Predictive Maintenance (PdM) 210 10.3.4 Quality Control and Yield 211 10.3.5 Troubleshooting 211 10.3.6 Supply Chain Management 212 10.3.7 Warehouse Management 213 10.3.8 Predicting Remaining Useful Life 214 10.3.9 Challenges 215 10.4 AI and ML-Based Drug Product Marketing 217 10.4.1 Product Launch 217 10.4.2 Real-Time Personalization and Consumer Behavior 218 10.4.3 Better Customer Relationships 219 10.4.4 Enhanced Marketing Measurement 220 10.4.5 Predictive Marketing Analytics 220 10.4.6 Price Dynamics 221 10.4.7 Market Segmentation 222 10.4.8 Challenges 223 10.5 Future Prospects and Way Forward 223 10.6 Conclusion 224 References 225 11 Artificial Intelligence and Machine Learning Applications in Vaccine Development 233Ali Sarmadi, Majid Hassanzadeganroudsari and M. Soltani 11.1 Introduction 234 11.2 Prioritizing Proteins as Vaccine Candidates 237 11.3 Predicting Binding Scores of Candidate Proteins 238 11.4 Predicting Potential Epitopes 243 11.5 Design of Multi-Epitope Vaccine 244 11.6 Tracking the RNA Mutations of a Virus 245 Conclusion 248 References 249 12 AI, ML and Other Bioinformatics Tools for Preclinical and Clinical Development of Drug Products 255Avinash Khadela, Sagar Popat, Jinal Ajabiya, Disha Valu, Shrinivas Savale and Vivek P. Chavda Abbreviations 256 12.1 Introduction 257 12.2 AI and ML for Pandemic 258 12.3 Advanced Analytical Tools Used in Preclinical and Clinical Development 259 12.3.1 Spectroscopic Techniques 260 12.3.2 Chromatographic Techniques 263 12.3.3 Electrochemical Techniques 263 12.3.4 Electrophoretic Techniques 264 12.3.5 Hyphenated Techniques 264 12.4 AI, ML, and Other Bioinformatics Tools for Preclinical Development of Drug Products 265 12.4.1 Various Computational Tools Used in Pre-Clinical Drug Development 266 12.5 AI, ML, and Other Bioinformatics Tools for Clinical Development of Drug Products 268 12.5.1 Role of AI, ML, and Bioinformatics in Clinical Research 270 12.5.2 Role of AI and ML in Clinical Study Protocol Optimization 272 12.5.3 Role of AI and ML in the Management of Clinical Trial Participants 272 12.5.4 Role of AI and ML in Clinical Trial Data Collection and Management 272 12.6 Way Forward 275 12.7 Conclusion 276 References 277 Part III: Bioinformatics Tools for Healthcare Sector 285 13 Artificial Intelligence and Machine Learning in Healthcare Sector 287Vivek P. Chavda, Kaushika Patel, Sachin Patel and Vasso Apostolopoulos Abbreviations 288 13.1 Introduction 288 13.2 The Exponential Rise of AI/ML Solutions in Healthcare 289 13.3 AI/ML Healthcare Solutions for Doctors 291 13.4 AI/ML Solution for Patients 293 13.5 AI Solutions for Administrators 295 13.6 Factors Affecting the AI/ML Implementation in the Healthcare Sector 297 13.6.1 High Cost 297 13.6.2 Lack of Creativity 298 13.6.3 Errors Potentially Harming Patients 298 13.6.4 Privacy Issues 298 13.6.5 Increase in Unemployment 299 13.6.6 Lack of Ethics 299 13.6.7 Promotes a Less-Effort Culture Among Human Workers 299 13.7 AI/ML Based Healthcare Start-Ups 299 13.8 Opportunities and Risks for Future 304 13.8.1 Patient Mobility Monitoring 305 13.8.2 Clinical Trials for Drug Development 305 13.8.3 Quality of Electronic Health Records (EHR) 305 13.8.4 Robot-Assisted Surgery 305 13.9 Conclusion and Perspectives 306 References 307 14 Role of Artificial Intelligence in Machine Learning for Diagnosis and Radiotherapy 315Sanket Chintawar, Vaishnavi Gattani, Shivanee Vyas and Shilpa Dawre Abbreviations 316 14.1 Introduction 317 14.2 Machine Learning Algorithm Models 318 14.2.1 Supervised Learning 318 14.2.2 Unsupervised Learning 319 14.2.3...