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• W4296608972 endingPage "802" @default.
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• W4296608972 abstract "Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates." @default.
• W4296608972 created "2022-09-23" @default.
• W4296608972 creator A5033154372 @default.
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• W4296608972 date "2022-09-14" @default.
• W4296608972 modified "2023-10-03" @default.
• W4296608972 title "Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation" @default.
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• W4296608972 doi "https://doi.org/10.1097/cad.0000000000001318" @default.
• W4296608972 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36136985" @default.
• W4296608972 hasPublicationYear "2022" @default.
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