q-RASAR: A Path to Predictive Cheminformatics - Arkaprava Banerjee,Kunal Roy
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Saadetis 12-18 tööpäeva jooksul
30-päevane tagastamisõigus
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 showca ... Täielik kirjeldus
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Kirjeldus
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.
Lisateave
| Autor | Arkaprava Banerjee, Kunal Roy |
|---|---|
| Kirjastaja | Springer Nature Switzerland |
| Series | SpringerBriefs in Molecular Science |
| Väljalaskeaasta | 2024 |
| Kaanetüüp | Pehme kaanega |
| EAN | 9783031520563 |