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 |