q-RASAR
A Path to Predictive Cheminformatics
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Beschreibung
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.
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.
Introduces the reader to a novel cheminformatic workflow Presents the genesis and model development Includes practical examples and software tools
Autor*in
Kunal Roy
Themen in »q-RASAR«
QSAR Read-across q-RASAR Cheminformatics Chemometrics Machine Learning Predictions Validation Data Gap Filling