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q-RASAR = a path to predictive cheminformatics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	q-RASAR/ by Kunal Roy, Arkaprava Banerjee.
其他題名:	a path to predictive cheminformatics /
作者:	Roy, Kunal.
其他作者:	Banerjee, Arkaprava.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	x, 91 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Cheminformatics. -
電子資源:	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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