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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| Główni autorzy: | , |
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| Format: | Elektroniczne E-book |
| Język: | angielski |
| Wydane: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Wydanie: | 1st ed. 2024. |
| Seria: | SpringerBriefs in Molecular Science,
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| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1007/978-3-031-52057-0 |
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| 100 | 1 | |a Roy, Kunal. |e author. |0 (orcid)0000-0003-4486-8074 |1 https://orcid.org/0000-0003-4486-8074 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a q-RASAR |h [electronic resource] : |b A Path to Predictive Cheminformatics / |c by Kunal Roy, Arkaprava Banerjee. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
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| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
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| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
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