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• W2045593617 abstract "The present paper deals with prediction of cytotoxic activity of 17-picolyl and 17-picolinylidene androstane derivatives toward androgen receptor negative prostate cancer cell line (PC-3). The prediction was achieved applying artificial neural networks (ANNs) method on the basis of molecular descriptors. The most important descriptors (skin permeability (SP), Madin-Darby canine kidney cell permeability (MDCK) and universal salt solubility factor (S+SF)) were selected by using stepwise selection coupled with partial least squares method. The ANN modelling was carried out in order to obtain reliable models which can facilitate further synthesis of androstane derivatives with high antiproliferative activity toward PC-3 cell line. The modelling procedure resulted in three ANN models with the best statistical performance. The obtained results show that the established ANN models can be applied for required purpose." @default.
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• W2045593617 date "2014-10-01" @default.
• W2045593617 modified "2023-10-18" @default.
• W2045593617 title "Non-linear assessment of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives – Chemometric guidelines for further syntheses" @default.
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• W2045593617 doi "https://doi.org/10.1016/j.ejps.2014.05.031" @default.
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