115,19 €
127,99 €
-10% with code: EXTRA
Q-Rasar
Q-Rasar
115,19
127,99 €
  • We will send in 10–14 business days.
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…
  • Publisher:
  • ISBN-10: 3031520564
  • ISBN-13: 9783031520563
  • Format: 15.6 x 23.4 x 0.6 cm, softcover
  • Language: English
  • SAVE -10% with code: EXTRA

Q-Rasar (e-book) (used book) | Kunal Roy | bookbook.eu

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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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  • Author: Kunal Roy
  • Publisher:
  • ISBN-10: 3031520564
  • ISBN-13: 9783031520563
  • Format: 15.6 x 23.4 x 0.6 cm, softcover
  • Language: English English

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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