Drug Design Using Machine Learning

• Autore: Inamuddin
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2022; ©2022
• Descrizione fisica: 1 online resource (378 pages)
• Soggetto genere / forma: Electronic books.
• Altri autori: AltalhiTariq A CruzJorddy N RefatMoamen Salah El-Deen
• Nota di contenuto: 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. ; 10.4.2 De Novo Synthesis of Bioactive Compounds.
• Titolo autorizzato: Drug Design Using Machine Learning
• ISBN: 1-394-16725-3; 1-394-16724-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910623989503321
• Lo trovi qui: Univ. Federico II