Synthetic Activators of Cell Migration Designed by Constructive Machine Learning
University Children’s Hospital Zurich, ETH Zurich
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.
Analysis was performed by TLC/MS using the Advion expression Compact Mass Spectrometer (CMS) and Plate Express TLC Plate Reader.