q-RASAR
A Path to Predictive Cheminformatics
Springer Nature Switzerland
ISBN 978-3-031-52057-0
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Bibliografische Daten
eBook. PDF
2024
X, 91 p. 20 illus., 17 illus. in color..
In englischer Sprache
Umfang: 91 S.
Verlag: Springer Nature Switzerland
ISBN: 978-3-031-52057-0
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: SpringerBriefs in Molecular Science
Produktbeschreibung
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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