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Record Nr. |
UNINA9910806198703321 |
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Autore |
Roy Kunal <1971-> |
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Titolo |
q-RASAR : A Path to Predictive Cheminformatics / / by Kunal Roy, Arkaprava Banerjee |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (99 pages) |
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Collana |
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SpringerBriefs in Molecular Science, , 2191-5415 |
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Disciplina |
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Soggetti |
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Chemistry - Data processing |
Quantum physics |
Computer simulation |
Molecules - Models |
Computational Chemistry |
Quantum Simulations |
Molecular Modelling |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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- |
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