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q-RASAR = a path to predictive cheminformatics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	q-RASAR/ by Kunal Roy, Arkaprava Banerjee.
Reminder of title:	a path to predictive cheminformatics /
Author:	Roy, Kunal.
other author:	Banerjee, Arkaprava.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	x, 91 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Cheminformatics. -
Online resource:	https://doi.org/10.1007/978-3-031-52057-0
ISBN:	9783031520570

q-RASAR = a path to predictive cheminformatics /
Roy, Kunal.

q-RASARa path to predictive cheminformatics /[electronic resource] :by Kunal Roy, Arkaprava Banerjee. - Cham :Springer Nature Switzerland :2024. - x, 91 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in molecular science,2191-5415. - SpringerBriefs in molecular science..

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.

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.

ISBN: 9783031520570

Standard No.: 10.1007/978-3-031-52057-0doiSubjects--Topical Terms:

605686
Cheminformatics.

LC Class. No.: QD39.3.E46

Dewey Class. No.: 542.85

q-RASAR = a path to predictive cheminformatics /
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300 $a x, 91 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in molecular science, $x 2191-5415
505 0 $a 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.
520 $a 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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650 0 $a Computational chemistry. $3 3350019
650 0 $a QSAR (Biochemistry) $3 684832
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