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• W2981454485 startingPage "1303" @default.
• W2981454485 abstract "Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties." @default.
• W2981454485 created "2019-11-01" @default.
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• W2981454485 date "2019-10-01" @default.
• W2981454485 modified "2023-10-15" @default.
• W2981454485 title "Synthetic Activators of Cell Migration Designed by Constructive Machine Learning" @default.
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• W2981454485 doi "https://doi.org/10.1002/open.201900222" @default.
• W2981454485 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6807213" @default.
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