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Record Nr. |
UNINA9910298635503321 |
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Autore |
Roy Kunal |
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Titolo |
A Primer on QSAR/QSPR Modeling : Fundamental Concepts / / by Kunal Roy, Supratik Kar, Rudra Narayan Das |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (129 p.) |
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Collana |
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SpringerBriefs in Molecular Science, , 2191-5407 |
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Disciplina |
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Soggetti |
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Chemistry, Physical and theoretical |
Chemometrics |
Bioinformatics |
Computational biology |
Theoretical and Computational Chemistry |
Math. Applications in Chemistry |
Computer Appl. in Life Sciences |
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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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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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QSAR/QSPR Modeling: Introduction -- Statistical methods in QSAR/QSPR -- QSAR/QSPR Methods -- Newer directions in QSAR/QSPR. |
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Sommario/riassunto |
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This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is |
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