Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

1st Edition - May 23, 2023
Imprint: Academic Press
Editor: Kunal Roy
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 6 3 8 - 7
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 6 3 9 - 4

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently… Read more

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Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.
The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates.

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Preface
Section 1: Introduction
Chapter 1: Quantitative structure-activity relationships (QSARs) in medicinal chemistry
Abstract
Acknowledgment
1: Introduction
2: QSAR: Background and its brief history
3: Chemical information and descriptors
4: The standard algorithm of QSAR modeling
5: Application of QSAR modeling in a real-world scenario: A case study example
6: Various challenges in QSAR modeling
7: Combinatorial library design for QSAR analysis
8: A few examples of successful application of QSAR in drug and pesticide design
9: Future avenues with concluding remarks
References
Chapter 2: Computer-aided drug design: An overview
Abstract
1: Introduction
2: Brief history
3: Databases used in drug discovery
4: Benchmarking techniques of computational tools in drug design and discovery
5: Scoring functions for evaluating protein-ligand complexes
6: Polypharmacology
7: Drug repositioning
8: Applications of computational tools in drug discovery
9: Summary
References
Chapter 3: Structure-based virtual screening in drug discovery
Abstract
Acknowledgments
1: Introduction
2: Structure-based virtual screening
3: Source of target structures
4: Source of the ligand structures
5: Software resources for SBVS
6: The process of structure-based virtual screening
7: Preparation of the ligand structures
8: Docking in structure-based virtual screening
9: Types of docking
10: The docking process
11: Software for docking
12: Assessment of the docking by using scoring function
13: Validation of the docking procedure
14: Concluding remarks
References
Chapter 4: The impact of artificial intelligence methods on drug design
Abstract
1: Introduction
2: The drug discovery process
3: From data to models
4: Bio-inspired learning systems
5: Symbolic and probabilistic AI methods
6: AI methods in drug development
7: Discussion and limitations
8: Conclusions
References
Section 2: Methods and case studies
Chapter 5: Graph machine learning in drug discovery
Abstract
Acknowledgments
1: Introduction
2: Primer to graph machine learning
3: Applications of GNNs in drug discovery
4: Conclusion
References
Chapter 6: Support vector machine in drug design
Abstract
Acknowledgment
1: Introduction
2: An intuitive description of SVM
3: SVM in drug design: Recent examples
4: Summary and outlook
References
Chapter 7: Understanding protein-ligand interactions using state-of-the-art computer simulation methods
Abstract
1: Introduction
2: Binding thermodynamics and methods to compute binding energy
3: Enhanced sampling methods to study ligand binding kinetics
4: State-of-the-art machine learning models
5: Datasets
6: Feature extraction
7: Modeling approaches
8: Binding affinity prediction
9: Binding site prediction
10: Miscellaneous
References
Chapter 8: Structure-based methods in drug design
Abstract
Acknowledgments
1: Introduction
2: Structure-based drug design methodologies
3: Case studies for some FDA approved drugs
4: Conclusions
References
Chapter 9: Structure-based virtual screening
Abstract
Acknowledgments
1: Introduction
2: 3D protein structure prediction
3: Importance of target-template modeling
4: Protein-protein interactions network analysis
5: Fragment-based de novo design
6: Drug target selection
7: Molecular docking and scoring functions
8: Molecular dynamics simulation
9: Structure-based pharmacophore modeling
10: ML-based scoring functions
11: Compound databases
12: Virtual screening
13: Recent trends from receptor-based virtual screening
14: Limitations of SBVS
15: Conclusions
References
Chapter 10: Deep learning for novel drug development
Abstract
Acknowledgments
1: Introduction
2: From shallow to deep neural nets
3: Specific neural network architectures
4: Further topics in deep learning for drug development
5: Cases studies
6: Discussion: Future prospects and challenges
References
Chapter 11: Computational methods in the analysis of viral-host interactions
Abstract
1: Introduction
2: Experimental approaches for virus-host interaction analysis
3: Computational analysis of virus-host interactions
4: Conclusions
References
Chapter 12: Chemical space and molecular descriptors for QSAR studies
Abstract
1: Introduction
2: Molecular descriptors in the current epistemological framework
3: Which characteristics molecular descriptors should have
4: Classification of molecular descriptors
5: Conclusions and new trends in molecular descriptors
References
Chapter 13: Machine learning methods in drug design
Abstract
1: Introduction
2: Classification of machine learning algorithms
3: Machine learning techniques in drug discovery
4: Overview and conclusions
References
Chapter 14: Deep learning methodologies in drug design
Abstract
1: Introduction
2: Molecular representation for deep learning
3: Predictive deep learning models
4: Deep learning frameworks for generating novel molecules
5: Deep learning frameworks for the de novo design of drugs with desired properties
6: Open source libraries for de novo drug design
7: Conclusion and future perspectives
References
Chapter 15: Molecular dynamics in predicting the stability of drug-receptor interactions
Abstract
Acknowledgments
1: Introduction
2: Theoretical background
3: Free energy calculations
4: Enhanced sampling methods for free energy calculations
5: Rapid/approximate methods for free energy calculation: Representation of solvent
6: Applications of PBSA/GBSA methods to determine the binding energetics of a ligand to its biomolecular target (receptor)
7: Combined quantum mechanical/molecular mechanical methods
8: Why molecular dynamics?
9: Some success stories of MD-based computer-aided drug discovery
10: Conclusion
References
Section 3: Special topics
Chapter 16: Toward models for bioaccumulation suitable for the pharmaceutical domain
Abstract
1: The biology of bioaccumulation
2: Empirical models to predict bioaccumulation
3: Active pharmaceutical ingredients data
4: The performance of existing models on pharma data and their AD
5: Toward a better qualification of model applicability domain: What really matters?
6: Covariate selection in the prediction of pharma bioaccumulation
7: A biological interpretation of our empirical results
8: Future developments
9: Conclusions
References
Chapter 17: Machine learning as a modeling approach for the account of nonlinear information in MIA-QSAR applications: A case study with SVM applied to antimalarial (aza)aurones
Abstract
1: Rationale
2: Expected results and deliverables
3: Workflow
4: Results
5: Learning and knowledge outcomes
References
Chapter 18: Deep learning using molecular image of chemical structure
Abstract
1: Introduction
2: Development
3: Quantitative structure-activity relationship as in silico approach
4: Deep learning
5: Image classification in deep learning
6: Deep learning-based QSAR
7: Parameter optimization in deep learning
8: Visualization of feature values extracted by deep learning
9: Concluding remarks
References
Chapter 19: Recent advances in deep learning enabled approaches for identification of molecules of therapeutics relevance
Abstract
Acknowledgments
1: Introduction
2: Deep learning
3: Current trends
4: Concluding remarks
References
Chapter 20: Computational toxicology of pharmaceuticals
Abstract
Authors’ contribution
1: Introduction
2: Development
3: Concluding remarks
References
Chapter 21: Ecotoxicological QSAR modeling and fate estimation of pharmaceuticals
Abstract
Author contributions
1: Introduction
2: QSAR/QSTR models for APIs
3: Interspecies quantitative structure-toxicity-toxicity relationship
4: QSAR models on mixture toxicity
5: QSAR models for environmentally-related fate parameters of pharmaceuticals
6: QSAR studies in the removal of pharmaceuticals
7: Conclusion
References
Chapter 22: Computational modeling of drugs for neglected diseases
Abstract
1: Introduction
2: Parasitic neglected diseases
3: Viral neglected diseases
4: Bacterial neglected diseases
5: Conclusions
References
Chapter 23: Modeling ADMET properties based on biomimetic chromatographic data
Abstract
1: Introduction
2: Biomimetic chromatography
3: The use of biomimetic chromatography in modeling ADMET properties
4: Conclusions
References
Chapter 24: A systematic chemoinformatic analysis of chemical space, scaffolds and antimicrobial activity of LpxC inhibitors
Abstract
Acknowledgment
1: Introduction
2: Development
3: Results and discussion
4: Conclusion and future perspective
References
Section 4: Tools and databases
Chapter 25: Tools and software for computer-aided drug design and discovery
Abstract
Acknowledgments
Declaration of competing interest
Funding
1: Drug discovery and computer-aided drug design
2: Role of tools and software in CADD
3: Types of tools and software in CADD
4: Overview and conclusion
References
Chapter 26: Machine learning resources for drug design
Abstract
1: Introduction
2: Machine learning for de novo design
3: Machine learning for drug target prediction
4: Machine learning for retrosynthesis
5: Machine learning for predictive toxicology
6: Machine learning for virtual screening
7: Machine learning in drug design: Case studies
8: Concluding remarks
References
Chapter 27: Building bioinformatics web applications with Streamlit
Abstract
1: Introduction
2: Benefits of building web applications for drug discovery
3: Challenges in building web applications
4: Streamlit web development framework
5: Extending Streamlit functionality with components
6: Bioinformatics web app built with Streamlit
7: Thought process of building Streamlit apps and components
8: Conclusions
References
Chapter 28: Free tools and databases in ligand and structure-based drug design
Abstract
Acknowledgments
1: Introduction
2: Types of CADD: Structure-based drug design and ligand-based drug design
3: Concluding remarks
References
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

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