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...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2024]
|
| Ausgabe: | 1st ed. 2024 |
| Schriftenreihe: | SpringerBriefs in molecular science
|
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | 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. |
| Beschreibung: | x, 91 Seiten Illustrationen 171 gr |
| ISBN: | 9783031520563 |
Internformat
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| 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 | ||
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| id | DE-604.BV049671292 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T07:55:00Z |
| institution | BVB |
| isbn | 9783031520563 |
| language | English |
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| physical | x, 91 Seiten Illustrationen 171 gr |
| publishDate | 2024 |
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| publisher | Springer |
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| series2 | SpringerBriefs in molecular science |
| spelling | Roy, Kunal 1971- Verfasser (DE-588)1185508783 aut q-RASAR a path to predictive cheminformatics Kunal Roy, Arkaprava Banerjee Cham Springer [2024] x, 91 Seiten Illustrationen 171 gr txt rdacontent n rdamedia nc rdacarrier SpringerBriefs in molecular science 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. 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 bicssc bisacsh Quantum physics Computer simulation Molecules—Models Chemistry—Data processing Hardcover, Softcover / Chemie/Theoretische Chemie Banerjee, Arkaprava Verfasser aut Erscheint auch als Online-Ausgabe 978-3-031-52057-0 |
| spellingShingle | Roy, Kunal 1971- Banerjee, Arkaprava q-RASAR a path to predictive cheminformatics bicssc bisacsh Quantum physics Computer simulation Molecules—Models Chemistry—Data processing |
| title | q-RASAR a path to predictive cheminformatics |
| title_auth | q-RASAR a path to predictive cheminformatics |
| title_exact_search | q-RASAR a path to predictive cheminformatics |
| title_full | q-RASAR a path to predictive cheminformatics Kunal Roy, Arkaprava Banerjee |
| title_fullStr | q-RASAR a path to predictive cheminformatics Kunal Roy, Arkaprava Banerjee |
| title_full_unstemmed | q-RASAR a path to predictive cheminformatics Kunal Roy, Arkaprava Banerjee |
| title_short | q-RASAR |
| title_sort | q rasar a path to predictive cheminformatics |
| title_sub | a path to predictive cheminformatics |
| topic | bicssc bisacsh Quantum physics Computer simulation Molecules—Models Chemistry—Data processing |
| topic_facet | bicssc bisacsh Quantum physics Computer simulation Molecules—Models Chemistry—Data processing |
| work_keys_str_mv | AT roykunal qrasarapathtopredictivecheminformatics AT banerjeearkaprava qrasarapathtopredictivecheminformatics |