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Product details:
- Edition number 1st ed. 2024
- Publisher Springer
- Date of Publication 26 January 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031520563
- Binding Paperback
- No. of pages91 pages
- Size 235x155 mm
- Weight 174 g
- Language English
- Illustrations 3 Illustrations, black & white; 17 Illustrations, color 582
Categories
Short description:
This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.
MoreLong description:
This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-RASAR models demonstrating practical examples. It also showcases successful case studies on the application of q-RASAR modeling in medicinal chemistry, predictive toxicology, and materials sciences. The book also includes the tools used for q-RASAR model development for new users. It is a valuable resource for researchers and students interested in grasping the development algorithm of q-RASAR models and their application within specific research domains.
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Table of Contents:
Chemical Information and Molecular Similarity.- Read-across and Quantitative Structure-activity Relationships (QSAR) for Making Predictions and Data Gap-Filling.- Quantitative Read-Across (q-RA) and Quantitative Read-Across Structure-Activity Relationships (q-RASAR) ? Genesis and Model Development.- Tools, Applications, and Case Studies (q-RA and q-RASAR).- Future Prospects.
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