q-RASAR A Path to Predictive Cheminformatics /
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...
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| Κύριοι συγγραφείς: | , |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Έκδοση: | 1st ed. 2024. |
| Σειρά: | SpringerBriefs in Molecular Science,
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| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.1007/978-3-031-52057-0 |
| Ετικέτες: |
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| Περίληψη: | 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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| Φυσική περιγραφή: | 1 online resource (X, 91 p. 20 illus.). 17 illus. in color. |
| ISBN: | 9783031520570 |
| ISSN: | 2191-5415 |
| DOI: | 10.1007/978-3-031-52057-0 |