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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jadhav, Hemant R. | - |
dc.date.accessioned | 2023-11-30T07:22:45Z | - |
dc.date.available | 2023-11-30T07:22:45Z | - |
dc.date.issued | 2014-09 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s00044-014-1240-5 | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/13282 | - |
dc.description.abstract | Quantitative structure–activity relationship (QSAR) is the statistical correlation of physicochemical properties of the structure with biological activity. QSAR study involves two main steps: first is the generation of descriptors, and second is building and validating the models. We have developed a python script that effectively uses descriptor and activity data in building and validating best QSAR model. Although available software provides descriptor calculation, lack of either the components needed for cross-validation or proper workflow for QSAR analysis is the bottleneck. In this paper we report, the validation results of this software using data available for MMP 13 inhibitors and anti-malarial compounds. We have found that the values obtained using this script correlate well with that calculated from Microsoft excel and reported QSAR models. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.subject | Pharmacy | en_US |
dc.subject | Quantitative structure–activity relationship (QSAR) | en_US |
dc.subject | QSAR | en_US |
dc.title | A new open source data analysis python script for QSAR study and its validation | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Pharmacy |
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