Drug Design using Machine Learning E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 617

Veröffentlichungsjahr: 2022

Beschreibung

DRUG DESIGN USING MACHINE LEARNING 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. 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. 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.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

1 Molecular Recognition and Machine Learning to Predict Protein-Ligand Interactions

1.1 Introduction

1.2 Molecular Docking

1.3 Machine Learning

1.4 Conclusions

References

2 Machine Learning Approaches to Improve Prediction of Target-Drug Interactions

2.1 Machine Learning Revolutionizing Drug Discovery

2.2 A Brief Summary of Machine Learning Models

2.3 Target Validation

2.4 Lead Discovery

2.5 Lead Optimization

2.6 Peptides in Pharmaceuticals

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

3.5 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

4.6 Conclusion

References

5 Drug Repurposing Based on Machine Learning

5.1 Introduction

5.2 Computational Drug Repositioning 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

5.6 Drugs Repurposing Through Machine Learning-Case Studies

5.7 Conclusion

References

6 Recent Advances in Drug Design With Machine Learning

6.1 Introduction

6.2 Categorization of Machine Learning Tasks

6.3 Machine Language-Mediated Predictive Models in Drug Design

6.4 Machine Learning Models

6.5 Machine Learning and Docking

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

7.3 Biodegradable Polymers

7.4 Drug Delivery

7.5 Conclusion

Acknowledgments

References

8 Neural Network for Screening Active Sites on Proteins

8.1 Introduction

8.2 Structural Proteomics

8.3 Gist Techniques to Study the Active Sites on Proteins

8.4 Neural Networking Algorithms to Study Active Sites on Proteins

8.5 Conclusion

References

9 Protein Redesign and Engineering Using Machine Learning

9.1 Introduction

9.2 Designing Sequence-Function Model Through Machine Learning

9.3 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

9.7 Force Field and Score Function

9.8 Machine Learning for Prediction of Hot Spots

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

10.3 Transcriptomics Approaches for the Selection of Bioactive Compounds

10.4 Artificial Intelligence Approaches for the Selection of Bioactive Compounds

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

11.1 Introduction

11.2 Drug Discovery

11.3 Drug Design Through New Techniques

11.4 Machine Learning as a Science

11.5 Reinforcement Machine Learning

11.6 AI Application in Drug Design

11.7 Machine Learning Methods Used in Drug Discovery

11.8 Deep Learning (DL)

11.9 Drug Design Applications

11.10 Drug Discovery Problems

11.11 Conclusion

References

12 Artificial Intelligence for Assessing Side Effects

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

Wiley End User License Agreement

List of Tables

Chapter 1

Table 1.1 Developments using machine learning (ML) algorithms in molecular docki...

Chapter 2

Table 2.1 Several databases which collected protein structures; approved or next...

Table 2.2 Survey of ML methods for LBS prediction.

Table 2.3 Survey of some ML methods for SF.

Chapter 3

Table 3.1 Drug discovery and development phases.

Table 3.2 Traditional versus new strategies in drug development process.

Table 3.3 Traditional drug development vs drug repurposing process [29].

Table 3.4 Open-source databases with molecular, or pharmacological information [...

Chapter 5

Table 5.1 Comparison between conventional drug discovery & development strategie...

Table 5.2 Data resources used for drug repurposing studies.

Table 5.3 Data resources based on the objectives of drug repurposing.

Table 5.4 Drug repurposing approaches.

Table 5.5 Drug examples repurposed through machine learning.

Table 5.6 Some herbal drugs repurposed through the machine learning program.

Chapter 6

Table 6.1 Algorithms/programs using ML methods for predicting druggability and/o...

Chapter 7

Table 7.1 Common fluids used for SCF technology (from the authors).

Table 7.2 Physicochemical properties of SCF in comparison to liquids and gases (...

Chapter 8

Table 8.1 Summarize of MI uses.

Table 8.2 Summarize of the main Ml program to study the PPIs.

Table 8.3 Essential information obtained by the NMR.

Chapter 9

Table 9.1 Various techniques of ensemble learning.

Table 9.2 Applications of machine learning in protein redesign.

Chapter 10

Table 10.1 Selected software approaches that use adaptive techniques for compoun...

Chapter 12

Table 12.1 Tools, models and networks for artificial intelligence.

Table 12.2 Networks for artificial intelligence.

List of Illustrations

Chapter 1

Figure 1.1 Molecular recognition models.

Figure 1.2 General protocol of docking work.

Figure 1.3 Conformational searching. (a) Rotatable bonds of two common drugs. (b...

Chapter 2

Figure 2.1 The overall process of developing a drug. Target identification and v...

Figure 2.2 SVM classification examples. (a) Linearly separable datasets. The sol...

Figure 2.3 (a) Decision tree example. The dataset in each decision node is divid...

Figure 2.4 Example of a fully connected neural network with one input layer (two...

Figure 2.5 (a) The sphere packing problem in 2 dimensions. Grid methods only che...

Figure 2.6 (a) SASA (red line) and SESA (blue bold line) in 2 dimensions with ci...

Figure 2.7 (a) The nails and elastic band analogy for the CH. The series of nail...

Figure 2.8 Generalization of the main categories between scoring functions. A fo...

Figure 2.9 Machine learning techniques applied to peptide-based drug development...

Chapter 3

Figure 3.1 Depiction of the phases of drug advancement, as well as Phase IV, whi...

Figure 3.2 Accelerated drug discovery process with machine learning and AI (redr...

Figure 3.3 Remarkable improvements by using AI in drug development process (redr...

Chapter 4

Figure 4.1 Example of a lead compound (salicylic acid) and its analog (acetyl sa...

Figure 4.2 Artificial intelligence in drug screening [12].

Figure 4.3 Schematic representations of the DL-PADC architecture [15].

Figure 4.4 Relationship between AI, ML, and DL [24].

Figure 4.5 CT analysis [38].

Figure 4.6 Wound variants [40].

Figure 4.7 Drug target interaction score [32].

Figure 4.8 Process for Parkinson’s [64].

Figure 4.9 Parkinson’s analysis [64].

Figure 4.10 Stem cell analysis [73].

Chapter 5

Figure 5.1 Drug repurposing strategies.

Figure 5.2 Types of machine learning methods.

Figure 5.3 Classification of data resources based on the type of data they conta...

Figure 5.4 Classification of data resources based on the objectives they aim to ...

Figure 5.5 Channel for text mining (TM) technique.

Figure 5.6 Workflow of semantics-based approaches.

Chapter 6

Figure 6.1 Approaches utilized in the process of drug discovery and two key fiel...

Figure 6.2 Categorization of machine learning tasks.

Figure 6.3 Supervised learning (left) and unsupervised learning (right).

Figure 6.4 Semisupervised learning (left) and reinforcement learning (right).

Figure 6.5 Workflows for QSAR and QSPR. Data are encoded in chemical descriptors...

Chapter 7

Figure 7.1 Main manufacturing methods for drug carrier systems (from the authors...

Figure 7.2 Model of an equilibrium diagram for a pure substance (from the author...

Figure 7.3 Biodegradation mechanisms (from the authors).

Figure 7.4 Classification of biodegradable polymers according to their origin an...

Figure 7.5 Uses of biodegradable polymers for drug loading using SCF technology ...

Figure 7.6 Main biologically derived polymers used with SCF technologies (from t...

Figure 7.7 Main synthetic polymers used with SCF technologies (from the authors)...

Figure 7.8 Main techniques with supercritical fluids used for drug loading (from...

Figure 7.9 General process of supercritical impregnation with CO

2

(from the auth...

Figure 7.10 General scheme of RESS process (from the authors).

Figure 7.11 General scheme of SAS process (from the authors).

Figure 7.12 General scheme of PGSS process (from the authors).

Figure 7.13 Applications of supercritical impregnation in biodegradable polymers...

Figure 7.14 Examples of drugs used in SCCO

2

impregnation (from the authors).

Figure 7.15 Different types of drugs used in supercritical impregnation with CO

2

...

Figure 7.16 Molecular weight vs. impregnation rate (from the authors).

Figure 7.17 Temperature versus impregnation graph (from the authors).

Figure 7.18 Effect of pressure on the percentage of impregnation with SCF (from ...

Chapter 8

Figure 8.1 Scheme representation of the interaction of enzyme and substrate thro...

Figure 8.2 Scheme representation of the whole process from a substrate to becomi...

Figure 8.3 Conventional and in silico techniques employed to study active sites ...

Figure 8.4 Scheme representation of the affinity chromatography.

Figure 8.5 Scheme representation of the coimmunoprecipitations technique.

Chapter 9

Figure 9.1 A simple illustration of EvoDesign server showing the steps in de nov...

Figure 9.2 Comparison of the major tasks in modelling proteins: Structure Predic...

Figure 9.3 The most general illustration to choose sequence function model for m...

Figure 9.4 A framework of three neural networks. A represents the network to est...

Figure 9.5 The process to design antibodies against SARS-CoV-2 using advanced te...

Chapter 10

Figure 10.1 Overview of RNA seq. rRNA: ribosomal RNA, mRNA: messenger RNA.

Figure 10.2 The context of deep learning virtual screening server deep screening...

Figure 10.3 The architecture of DNN based classification model.

Figure 10.4 Illustration graph of convolutional neural networks (CNNs).

Figure 10.5 Machine learning applications within a “functional biosynthetic gene...

Chapter 11

Figure 11.1 Steps of drug discovery.

Figure 11.2 Machine learning category.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Index

End User License Agreement

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Scrivener Publishing

100 Cummings Center, Suite 541J

Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

Drug Design Using Machine Learning

Edited by

Inamuddin

Tariq Altalhi

Jorddy N. Cruz

and

Moamen Salah El-Deen Refat

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 9781394166282

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Preface

Traditionally, the design of new drugs has been a long process that requires an investment of billions of dollars. In the last decades, molecular modeling techniques have been used in the pharmaceutical industry and large laboratories to accelerate this process in order to reduce the amount of money that needs to be invested. More recently, machine learning approaches to drug discovery have aroused great interest in the scientific community. This has occurred, among other reasons, due to advances in high-performance computing and the availability of an abundance of biological and chemical information on thousands of compounds. Through machine learning and artificial intelligence approaches, this information can be filtered quickly and at a low cost. From this, algorithms can be trained to be used in the different stages of drug design.

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 book should be useful for information technology professionals, pharmaceutical industry workers, engineers, university students and faculty members, medical practitioners, researchers and laboratory workers who have a keen interest in the area of machine learning and artificial intelligence approaches applied to drug advancements. A chapter-by-chapter summary of the work reported in the 12 chapters of this book follows.

–

Chapter 1

describes the use of molecular recognition for drug development through various mathematical models. Molecular docking of the main elements is discussed in detail to consider which elements are important for obtaining a reliable prediction of protein-ligand complexes. The role of machine learning in molecular recognition models is also analyzed.

–

Chapter 2

reviews classical and machine learning-based approaches to study target-drug interactions in the field of drug discovery, from target identification to the optimization of the lead compound. In addition, a special section discusses peptide-based drugs.

–

Chapter 3

gives a brief overview of the various machine learning techniques that underpin artificial intelligence, poly-pharmacology, and drug repurposing to improve healthcare services. The way in which machine learning is used throughout the drug development process to help increase its efficacy and robustness, resulting in a significant reduction of the time and cost of bringing new drugs to market, is discussed in detail.

–

Chapter 4

elaborates on the advancements in artificial intelligence technologies along with their applications, enumerates the challenges faced by these technologies that retard their full-scale implementation, and also provides an overview of their social, legal, and economic aspects. All these are discussed for various applications such as drug delivery, healthcare, and medical systems.

–

Chapter 5

details the various machine learning approaches for drug repurposing. The network-based approach, text mining-based approach, and semantics-based approach are discussed in fair detail. Furthermore, case studies of drugs repurposed through machine learning programs are also discussed.

–

Chapter 6

summarizes the recent developments in the machine learning-mediated drug discovery process within industrial and academic contexts. More precisely, machine learning algorithms used for drug discovery, bioactivity prediction using machine learning, and application of machine learning in chemo-informatics are reviewed. Furthermore, an in-depth analysis of the challenges and suggestions are provided.

–

Chapter 7

details the basic principles underlying supercritical fluid technology and its main techniques. Furthermore, the more representative biodegradable polymers impregnated with supercritical fluid technology are described. Finally, the state of the art of supercritical fluid technology for improving drug absorption in biopolymer and the delivery processes are thoroughly reviewed.

–

Chapter 8

details the different

in vivo, in vitro

and

in silico

techniques applied to study the protein-protein interactions through the active sites. The role of the active sites of protein is also discussed. Moreover, databases and algorithms are mentioned along with their uses and advantages.

–

Chapter 9

describes various machine-learning methods involved in protein redesign and engineering along with strategies ranging from designing the model to predicting hot spots. The chapter also focuses on additional support vector machines, nearest neighbor, decision trees, neural networks, Bayesian networks, ensemble learning, and deep learning.

–

Chapter 10

describes computational methods used for the selection of bioactive compounds. In this context, various approaches based on transcriptomics and artificial intelligence are discussed. Additionally, methods and applications of de novo synthesis are also addressed along with its future endeavors in drug designing.

–

Chapter 11

discusses the application of computer-aided techniques for the prediction of drug effectiveness and toxicity, including artificial intelligence, artificial neural networks, and machine learning. A hierarchical method for drug design is followed as drug discovery, drug design through new techniques and application, machine learning methods, deep learning, applications, and problems.

–

Chapter 12

details the use of artificial intelligence in assessing the side effects to drugs. Practicing artificial intelligence necessitates the skills and awareness for data-intensive analysis, knowledge-based management, and definite challenges. A smarter future can be envisaged using artificial intelligence-guided new scientific accomplishments in the field of pharmacovigilance.

The EditorsInamuddinTariq AltalhiJorddy N. CruzMoamen Salah El-Deen RefatJuly 2022

2Machine Learning Approaches to Improve Prediction of Target-Drug Interactions

Balatti, Galo E.1*, Barletta, Patricio G.2, Perez, Andres, D.3, Giudicessi, Silvana L.4,5 and Martínez-Ceron, María C.4,6†

1Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Roquez Sáenz-Peña, Bernal, Argentina

2International Center for Theoretical Physics, Trieste, Italia

3IFLP, CONICET - Dpto. de Física, Universidad Nacional de La Plata, La Plata, Argentina

4