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dc.contributor.authorJadhav, Hemant R.-
dc.date.accessioned2023-11-30T07:22:45Z-
dc.date.available2023-11-30T07:22:45Z-
dc.date.issued2014-09-
dc.identifier.urihttps://link.springer.com/article/10.1007/s00044-014-1240-5-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/13282-
dc.description.abstractQuantitative 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.isoenen_US
dc.publisherSpringeren_US
dc.subjectPharmacyen_US
dc.subjectQuantitative structure–activity relationship (QSAR)en_US
dc.subjectQSARen_US
dc.titleA new open source data analysis python script for QSAR study and its validationen_US
dc.typeArticleen_US
Appears in Collections:Department of Pharmacy

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