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


Bruns, Dominique; Gawehn, Erik; Kumar, Karthiga Santhana; Schneider, Petra; Baumgartner, Martin; Schneider, Gisbert (2020). Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning. Chembiochem, 21(4):500-507.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:17 February 2020
Deposited On:07 Feb 2020 09:58
Last Modified:21 Feb 2020 02:04
Publisher:Wiley-VCH Verlag
ISSN:1439-4227
OA Status:Closed
Publisher DOI:https://doi.org/10.1002/cbic.201900346
PubMed ID:31418992

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