Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning

Chembiochem. 2020 Feb 17;21(4):500-507. doi: 10.1002/cbic.201900346. Epub 2019 Nov 14.


Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.

Keywords: artificial intelligence; convolutional neural network; drug discovery; medulloblastoma; virtual screening.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Line, Tumor
  • Cell Movement*
  • Deep Learning*
  • Drug Design
  • Humans
  • Receptors, CXCR4 / agonists*
  • Receptors, CXCR4 / antagonists & inhibitors*


  • CXCR4 protein, human
  • Receptors, CXCR4