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

Book details
Table of contents
Citations

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

Frequently asked questions

Simply head over to the account section in settings and click on “Cancel Subscription” - it’s as simple as that. After you cancel, your membership will stay active for the remainder of the time you’ve paid for. Learn more here.
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
Both plans give you full access to the library and all of Perlego’s features. The only differences are the price and subscription period: With the annual plan you’ll save around 30% compared to 12 months on the monthly plan.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
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 Medicina & Farmacologia. We have over one million books available in our catalogue for you to explore.

Information

Year
2023
ISBN
9780443186394

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Contributors
  7. Preface
  8. Section 1: Introduction
  9. Section 2: Methods and case studies
  10. Section 3: Special topics
  11. Section 4: Tools and databases
  12. Index