http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource : illustrations (chiefly color)
The objective of this book is to bring together several chapters that function as an overview of the use of machine learning and artificial intelligence applied to drug development. The initial chapters discuss drug-target interactions through machine learning for improving drug delivery, healthcare, and medical systems. Further chapters also provide topics on drug repurposing through machine learning, drug designing, and ultimately discuss drug combinations prescribed for patients with multiple or complex ailments.
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Molecular Recognition and Machine Learning to Predict Protein-Ligand Interactions -- 1.1 Introduction -- 1.1.1 Molecular Recognition -- 1.2 Molecular Docking -- 1.2.1 Conformational Search Algorithm -- 1.2.2 Scoring Function with Conventional Methods -- 1.3 Machine Learning -- 1.3.1 Machine Learning in Molecular Docking -- 1.3.2 Machine Learning Challenges in Molecular Docking -- 1.4 Conclusions -- References -- 2 Machine Learning Approaches to Improve Prediction of Target-Drug Interactions -- 2.1 Machine Learning Revolutionizing Drug Discovery -- 2.1.1 Introduction -- 2.1.2 Virtual Screening and Rational Drug Design -- 2.1.3 Small Organic Molecules and Peptides as Drugs -- 2.2 A Brief Summary of Machine Learning Models -- 2.2.1 Support Vector Machines (SVM) -- 2.2.2 Random Forests (RF) -- 2.2.3 Gradient Boosting Decision Tree -- 2.2.4 K-Nearest Neighbor (KNN) -- 2.2.5 Neural Network and Deep Learning -- 2.2.6 Gaussian Process Regression -- 2.2.7 Evaluating Regression Methods -- 2.2.8 Evaluating Classification Methods -- 2.3 Target Validation -- 2.3.1 Ligand Binding Site Prediction (LBS) -- 2.3.2 Classical Approaches -- 2.3.3 Machine Learning Approaches -- 2.3.3.1 SVM-Based Approaches -- 2.3.3.2 Random Forest-Based Approaches -- 2.3.3.3 Deep Learning-Based Approaches -- 2.4 Lead Discovery -- 2.4.1 The Relevance of Predict Binding Affinity -- 2.4.2 The Concept of Docking -- 2.4.3 The Scoring Function -- 2.4.4 Developing of Novels Scoring Functions by Machine Learning -- 2.4.4.1 Random Forests -- 2.4.4.2 Support Vector Machines -- 2.4.4.3 Neural Networks -- 2.4.4.4 Gradient Boosting Decision Tree -- 2.5 Lead Optimization -- 2.5.1 QSAR and Proteochemometrics -- 2.5.2 Machine Learning Algorithms in Deriving Descriptors.
2.6 Peptides in Pharmaceuticals -- 2.6.1 Peptide Natural and Synthetic Sources -- 2.6.2 Applications and Market for Peptides-Based Drugs -- 2.6.3 Challenges to Become a Peptide Into a Drug -- 2.6.4 Improving Peptide Drug Development Using Machine Learning Techniques -- 2.7 Conclusions -- References -- 3 Machine Learning Applications in Rational Drug Discovery -- 3.1 Introduction -- 3.2 The Drug Development and Approval Process -- 3.3 Human-AI Partnership -- 3.4 AI in Understanding the Pathway to Assess the Side Effects -- 3.4.1 Traditional Versus New Strategies in Drug Discovery -- 3.4.2 Target Identification and Authentication -- 3.4.3 Searching the Hit and Lead Molecules with the Help of AI -- 3.4.4 Discretion of a Population for Medical Trials Using AI -- 3.5 Predicting the Side Effects Using AI -- 3.6 AI for Polypharmacology and Repurposing -- 3.7 The Challenge of Keeping Drugs Safe -- 3.8 Conclusion -- Resources -- References -- 4 Deep Learning for the Selection of Multiple Analogs -- 4.1 Introduction -- 4.2 Goals of Analog Design -- 4.3 Deep Learning in Drug Discovery -- 4.4 Chloroquine Analogs -- 4.5 Deep Learning in Medical Field -- 4.5.1 Scientific Study of Skin Diseases -- 4.5.2 Anatomical Laparoscopy -- 4.5.3 Angiography -- 4.5.4 Interpretation of Wound -- 4.5.5 Molecular Docking -- 4.5.6 Breast Cancer Detection -- 4.5.7 Polycystic Organs -- 4.5.8 Bone Tissue -- 4.5.9 Interaction Drug-Target -- 4.5.10 Pancreatic Issue Prediction -- 4.5.11 Prediction of Carcinoma in Cells -- 4.5.12 Determining Parkinson's -- 4.5.13 Segregating Cells -- 4.6 Conclusion -- References -- 5 Drug Repurposing Based on Machine Learning -- 5.1 Introduction -- 5.2 Computational Drug Repositioning Strategies -- 5.2.1 Drug-Based Strategies -- 5.2.2 Disease-Based Strategies -- 5.3 Machine Learning.
5.4 Data Resources Used for Computational Drug Repositioning Through Machine Learning Techniques -- 5.5 Machine Learning Approaches Used for Drug Repurposing -- 5.5.1 Network-Based Approaches -- 5.5.2 Text Mining-Based Approaches -- 5.5.3 Semantics-Based Approaches -- 5.6 Drugs Repurposing Through Machine Learning-Case Studies -- 5.6.1 Psychiatric Disorders -- 5.6.2 Alzheimer's Disease -- 5.6.3 Drug Repurposing for Cancer -- 5.6.4 COVID-19 -- 5.6.5 Herbal Drugs -- 5.7 Conclusion -- References -- 6 Recent Advances in Drug Design With Machine Learning -- 6.1 Introduction -- 6.2 Categorization of Machine Learning Tasks -- 6.2.1 Supervised Learning -- 6.2.2 Unsupervised Learning -- 6.2.3 Semisupervised Learning -- 6.2.4 Reinforcement Learning -- 6.3 Machine Language-Mediated Predictive Models in Drug Design -- 6.3.1 Quantitative Structure-Activity Relationship Models (QSAR) -- 6.3.2 Quantitative Structure-Property Relationship Models (QSPR) -- 6.3.3 Quantitative Structure Toxicity Relationship Models (QSTR) -- 6.3.4 Quantitative Structure Biodegradability Relationship Models (QSBR) -- 6.4 Machine Learning Models -- 6.4.1 Artificial Neural Networks (ANNs) -- 6.4.2 Self-Organizing Map (SOM) -- 6.4.3 Multilayer Perceptrons (MLPs) -- 6.4.4 Counter Propagation Neural Networks (CPNN) -- 6.4.5 Bayesian Neural Networks (BNNs) -- 6.4.6 Support Vector Machines (SVMs) -- 6.4.7 Naive Bayesian Classifier -- 6.4.8 K Nearest Neighbors (KNN) -- 6.4.9 Ensemble Methods -- 6.4.9.1 Boosting -- 6.4.9.2 Bagging -- 6.4.10 Random Forest -- 6.4.11 Deep Learning -- 6.4.12 Synthetic Minority Oversampling Technique -- 6.5 Machine Learning and Docking -- 6.5.1 Scoring Power -- 6.5.2 Ranking Power -- 6.5.3 Docking Power -- 6.5.4 Predicting Docking Score Using Machine Learning -- 6.6 Machine Learning in Chemoinformatics.
6.7 Challenges and Limitations for Machine Learning in Drug Discovery -- 6.8 Conclusion and Future Perspectives -- References -- 7 Loading of Drugs in Biodegradable Polymers Using Supercritical Fluid Technology -- 7.1 Introduction -- 7.2 Supercritical Fluid Technology -- 7.2.1 Supercritical Fluids -- 7.2.2 Physicochemical Properties -- 7.2.3 Carbon Dioxide -- 7.3 Biodegradable Polymers -- 7.3.1 Main Biologically-Derived Polymers Used With SCF Technologies -- 7.3.1.1 Cellulose -- 7.3.1.2 Chitosan -- 7.3.1.3 Alginate -- 7.3.1.4 Collagen -- 7.3.2 Main Synthetic Polymers Used With SCF Technologies -- 7.3.2.1 Polylactic Acid (PLA) -- 7.3.2.2 Poly (Lactic-co-Glycolic Acid) (PLGA) -- 7.3.2.3 Polycaprolactone (PCL) -- 7.3.2.4 Poly (Vinyl Alcohol) (PVA) -- 7.4 Drug Delivery -- 7.4.1 Types of Drugs -- 7.4.2 Influence of Experimental Conditions on the Drug Loading -- 7.5 Conclusion -- Acknowledgments -- References -- 8 Neural Network for Screening Active Sites on Proteins -- 8.1 Introduction -- 8.2 Structural Proteomics -- 8.2.1 PPIs -- 8.2.2 Active Sites in Proteins -- 8.3 Gist Techniques to Study the Active Sites on Proteins -- 8.3.1 In Vitro -- 8.3.1.1 Affinity Purification -- 8.3.1.2 Affinity Chromatography -- 8.3.1.3 Coimmunoprecipitation -- 8.3.1.4 Protein Arrays -- 8.3.1.5 Protein Fragment Complementation -- 8.3.1.6 Phage Display -- 8.3.1.7 X-Ray Crystallography -- 8.3.1.8 Nuclear Magnetic Resonance Spectroscopy (NMR) -- 8.3.2 In Vivo -- 8.3.2.1 In-Silico Two-Hybrid -- 8.3.3 In-Silico and Neural Network -- 8.3.3.1 Data Base -- 8.3.3.2 Sequence-Based Approaches -- 8.3.3.3 Structure-Based Approaches -- 8.3.3.4 Phylogenetic Tree -- 8.3.3.5 Gene Fusion -- 8.4 Neural Networking Algorithms to Study Active Sites on Proteins -- 8.4.1 PDBSiteScan Program -- 8.4.2 Patterns in Nonhomologous Tertiary Structures (PINTS) -- 8.4.3 Genetic Active Site Search (GASS).
8.4.4 Site Map -- 8.4.5 Computed Atlas of Surface Topography of Proteins (CASTp) -- 8.5 Conclusion -- References -- 9 Protein Redesign and Engineering Using Machine Learning -- 9.1 Introduction -- 9.2 Designing Sequence-Function Model Through Machine Learning -- 9.2.1 Training of Model and Evaluation -- 9.2.2 Representation of Proteins by Vector -- 9.2.3 Guiding Exploration by Employing Sequence-Function Prediction -- 9.3 Features Based on Energy -- 9.4 Features Based on Structure -- 9.5 Prediction of Thermostability of Protein with Single Point Mutations -- 9.6 Selection of Features -- 9.6.1 Extraction of Features -- 9.7 Force Field and Score Function -- 9.8 Machine Learning for Prediction of Hot Spots -- 9.8.1 Support Vector Machines -- 9.8.2 Nearest Neighbor -- 9.8.3 Decision Trees -- 9.8.4 Neural Networks -- 9.8.5 Bayesian Networks -- 9.8.6 Ensemble Learning -- 9.9 Deep Learning-Neural Network in Computational Protein Designing -- 9.10 Machine Learning in Engineering of Proteins -- 9.11 Conclusion -- References -- 10 Role of Transcriptomics and Artificial Intelligence Approaches for the Selection of Bioactive Compounds -- 10.1 Introduction -- 10.2 Types of Bioactive Compounds -- 10.2.1 Phenolic Acids -- 10.2.2 Stilbenes -- 10.2.3 Ellagitannins -- 10.2.4 Flavonoids -- 10.2.5 Proanthocyanidin -- 10.2.6 Vitamins -- 10.2.7 Bioactive Peptides -- 10.3 Transcriptomics Approaches for the Selection of Bioactive Compounds -- 10.3.1 Hybrid Transcriptome Sequencing -- 10.3.2 Microarray -- 10.3.3 RNA-Seq -- 10.4 Artificial Intelligence Approaches for the Selection of Bioactive Compounds -- 10.4.1 Machines Learning (ML) Approach for the Selection of Bioactive Compounds -- 10.4.1.1 Evolution of Machine Learning to Deep Learning -- 10.4.1.2 Virtual Screening -- 10.4.1.3 Recent Advances in Machine Learning -- 10.4.1.4 Deep Learning.
edited by Inamuddin, Tariq Altalhi, Jorddy N. Cruz and Moamen Salah El-Deen Refat.
Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
Includes bibliographical references and index.