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Drug Design using Machine Learning

Edited by Inamuddin, Tariq Altalhi, Jorddy N. Cruz, Moamen Salah El-Deen Refat
Copyright: 2022   |   Status: Published
ISBN: 9781394166282  |  Hardcover  |  
375 pages
Price: $195 USD
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One Line Description
The use of machine learning algorithms in drug discovery has accelerated in recent years and this book provides an in-depth overview of the still-evolving field.

Audience
The book will be useful for information technology professionals, pharmaceutical industry workers, engineers, university researchers, medical practitioners, and laboratory workers who have a keen interest in the area of machine learning and artificial intelligence approaches applied to drug advancements.

Description
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.
This excellent overview
• Provides a broad synopsis of machine learning and artificial intelligence applications to the advancement of drugs;
• Details the use of molecular recognition for drug development through various mathematical models;
• Highlights classical as well as machine learning-based approaches to study target-drug interactions in the field of drug discovery;
• Explores computer-aided technics for prediction of drug effectiveness and toxicity.

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Author / Editor Details
Inamuddin, PhD, is an assistant professor at King Abdulaziz University, Jeddah, Saudi Arabia, and is also an assistant professor in the Department of Applied Chemistry, Aligarh Muslim University, Aligarh, India. He has extensive research experience in multidisciplinary fields of analytical chemistry, materials chemistry, electrochemistry, renewable energy, and environmental science. He has published about 190 research articles in various international scientific journals, 18 book chapters, and edited 60 books.

Tariq Altalhi is Head of the Department of Chemistry and Vice Dean of Science College at Taif University, Saudi Arabia. He received his PhD from the University of Adelaide, Australia in 2014. His research interests include developing advanced chemistry-based solutions for solid and liquid municipal waste management, converting plastic bags to carbon nanotubes, and fly ash to efficient adsorbent material.

Jorddy Neves Cruz is a researcher at the Federal University of Pará and the Emilio Goeldi Museum, Brazil. He has experience in multidisciplinary research in the areas of medicinal chemistry, drug design, extraction of bioactive compounds, extraction of essential oils, food chemistry, and biological testing.

Moamen Salah El-Deen Refat is a professor of Inorganic Chemistry at the Department of Chemistry Science at Taif University, Saudi Arabia. He has received multiple prizes such as the Distinguished Researcher Award, Taif University from 2009-2021, Gold Medal Telesio-Galilei Academy of Science for pioneering work in chemistry in 2013, and the Arab Prize in Chemistry for Young Arab Researchers in 2010.

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Table of Contents
Preface
1. Molecular Recognition and Machine Learning to Predict Protein-Ligand Interactions
A. Reyes Chaparro, J.A. Moreno-Melendres, A.L. Ramos-Jacques and A.R. Hernandez-Martinez
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
Balatti, Galo E., Barletta, Patricio G., Perez, Andres, D., Giudicessi, Silvana L. and Martínez-Ceron, María C.
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
Hemanshi Chugh and Sonal Singh
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
C. Deepa, D. Balaji, V. Bhuvaneswari, L. Rajeshkumar, M. Ramesh and M. Priyadharshini
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
Laxmi Tripathi, Praveen Kumar, Kalpana Swain and Satyanarayan Pattnaik
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
Muhammad Faisal
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
Janet de los Angeles Chinellato Díaz, Santiago Fernandez Bordín, Facundo Mattea
and Marcelo Ricardo Romero
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
Johanna Bustamante-Torres, Samantha Pardo and Moises Bustamante-Torres
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
Zhuha Basit, Hira Akram, Muhammad Mudassir Iqbal, Gulzar Muhammad, Muhammad Shahbaz Aslam, Iram Gul, Muhammad Jamil and Mudassir Hussain Tahir
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
Roshan Zameer, Sana Tariq, Sana Noreen, Muhammad Sadaqat and Farrukh Azeem
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
10.4.2.1 Application Examples of De Novo Design
10.4.3 Applications of Machine Learning and Deep Learning
10.4.3.1 Application of Deep Learning in Compound Activity and Property Prediction
10.4.3.2 Application of Deep Learning in Biological Imaging Analysis
10.4.3.3 Future Development of Deep Learning in Drug Discovery
10.5 Applications of Transcriptomic and Artificial Intelligence Techniques for Drug Discovery
10.6 Conclusion and Perspectives
References
11. Prediction of Drug Toxicity Through Machine Learning
Ariga Gharabeiki, Foad Monemian and Ali Kargari
11.1 Introduction
11.2 Drug Discovery
11.2.1 Target Identification
11.2.2 Lead Discovery: Preclinical
11.2.3 Medicinal Chemistry: Preclinical
11.2.4 In Vitro Studies
11.2.5 In Vivo Studies
11.2.6 Clinical Trials
11.2.7 Food and Drug Administration Approval
11.3 Drug Design Through New Techniques
11.4 Machine Learning as a Science
11.4.1 Supervised Machine Learning
11.4.2 Unsupervised Machine Learning
11.5 Reinforcement Machine Learning
11.6 AI Application in Drug Design
11.7 Machine Learning Methods Used in Drug Discovery
11.7.1 Support Vector Machines
11.7.2 Random Forest
11.7.3 Multilayer Perception (MLP)
11.8 Deep Learning (DL)
11.9 Drug Design Applications
11.10 Drug Discovery Problems
11.10.1 Prognostic Biomarkers
11.10.2 Digital Pathology
11.11 Conclusion
References
12. Artificial Intelligence for Assessing Side Effects
Aarati Panchbhai
12.1 Introduction
12.2 Background
12.3 Traditional Approach to Pharmacovigilance and Its Limitations
12.4 Role of Artificial Intelligence in Pharmacological Profiling for Safety Assessment
12.5 Artificial Intelligence for Assessing Side Effects
12.6 Conclusion
References
Index

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