Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/7061
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dc.contributor.authorPreeti M. Chincholikar-
dc.contributor.authorSulbha Amlathe-
dc.date.accessioned2024-02-27T06:05:32Z-
dc.date.available2024-02-27T06:05:32Z-
dc.date.issued2015-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/7061-
dc.description.abstractA quantitative structure- activity relationship (QSAR) study was performed to develop models those relate the structures of 41 curcumin compounds to their n-octanol- water partition coefficients (log P01w) - The analogues were studied under different quantum-chemical descriptors, electrostatic, constitutional, topological, geometrical that were considered as inputs to the model. The models were constructed using 20 molecules as training set, and predictive ability tested using 11 compounds. Modelling of log P01w of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR). The usefulness of the quantum chemical descriptors, calculated at the level of the HF theories using 6-310* basis set/or QSAR study of anti-cancer drugs was examined. A multi-parametric equation containing maximum eight descriptors at HF/6-31G* method with good statistical qualities (R2train=0.838, Ftrain=22.93, 9:wo=0.843,R10 41=0.862,Q2 wo=O. 729) was obtained by Multiple Linear Regression using stepwise method. The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: crossvalidation, validation through an external test set, and Y randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds.-
dc.publisherScience Technology and Management Journal of Aisect University-
dc.titleCurcumin Analogues and Their Logpo/W Prediction-
dc.volVol IV-
dc.issuedNo VII-
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