Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development
eBook - ePub

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

  1. 768 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development

About this book

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. - Presents chemometrics, cheminformatics and machine learning methods under a single reference - Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design - Highlights special topics of computational drug design and available tools and databases

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Yes, you can access Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development by Kunal Roy in PDF and/or ePUB format, as well as other popular books in Medicine & Pharmaceutical, Biotechnology & Healthcare Industry. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Front Matter
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Contributors
  7. Preface
  8. List of Illustrations
  9. List of Tables
  10. Chapter 1 : Quantitative structure-activity relationships (QSARs) in medicinal chemistry
  11. Chapter 2 : Computer-aided drug design: An overview
  12. Chapter 3 : Structure-based virtual screening in drug discovery
  13. Chapter 4 : The impact of artificial intelligence methods on drug design
  14. Chapter 5 : Graph machine learning in drug discovery
  15. Chapter 6 : Support vector machine in drug design
  16. Chapter 7 : Understanding protein-ligand interactions using state-of-the-art computer simulation methods
  17. Chapter 8 : Structure-based methods in drug design
  18. Chapter 9 : Structure-based virtual screening
  19. Chapter 10 : Deep learning for novel drug development
  20. Chapter 11 : Computational methods in the analysis of viral-host interactions
  21. Chapter 12 : Chemical space and molecular descriptors for QSAR studies
  22. Chapter 13 : Machine learning methods in drug design
  23. Chapter 14 : Deep learning methodologies in drug design
  24. Chapter 15 : Molecular dynamics in predicting the stability of drug-receptor interactions
  25. Chapter 16 : Toward models for bioaccumulation suitable for the pharmaceutical domain
  26. 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
  27. Chapter 18 : Deep learning using molecular image of chemical structure
  28. Chapter 19 : Recent advances in deep learning enabled approaches for identification of molecules of therapeutics relevance
  29. Chapter 20 : Computational toxicology of pharmaceuticals
  30. Chapter 21 : Ecotoxicological QSAR modeling and fate estimation of pharmaceuticals
  31. Chapter 22 : Computational modeling of drugs for neglected diseases
  32. Chapter 23 : Modeling ADMET properties based on biomimetic chromatographic data
  33. Chapter 24 : A systematic chemoinformatic analysis of chemical space, scaffolds and antimicrobial activity of LpxC inhibitors
  34. Chapter 25 : Tools and software for computer-aided drug design and discovery
  35. Chapter 26 : Machine learning resources for drug design
  36. Chapter 27 : Building bioinformatics web applications with Streamlit
  37. Chapter 28 : Free tools and databases in ligand and structure-based drug design
  38. Index
  39. A