Synthetic Activators of Cell Migration Designed by Constructive Machine Learning
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000376111Publication status
publishedExternal links
Journal / series
ChemistryOpenVolume
Pages / Article No.
Publisher
WileySubject
chemoinformatics; chemotaxis; drug discovery; neural networks; phenotypic screeningOrganisational unit
03852 - Schneider, Gisbert / Schneider, Gisbert
Funding
159737 - Computational design of custom-tailored chemokine receptor blockers (SNF)
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