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• W1818070918 abstract "The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer-generated sequences featured improved cancer-cell penetration, induced cancer-cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co-administered chemotherapeutic agents in vitro. This study demonstrates the potential of multidimensional machine-learning methods for rapidly obtaining peptides with the desired cellular activities." @default.
• W1818070918 created "2016-06-24" @default.
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• W1818070918 date "2015-06-26" @default.
• W1818070918 modified "2023-10-03" @default.
• W1818070918 title "Multidimensional Design of Anticancer Peptides" @default.
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• W1818070918 doi "https://doi.org/10.1002/anie.201504018" @default.
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• W1818070918 hasPublicationYear "2015" @default.
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