Publication: Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning
Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning
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Bruns, D., Gawehn, E., Kumar, K. S., Schneider, P., Baumgartner, M., & Schneider, G. (2020). Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning. Chembiochem, 21, 500–507. https://doi.org/10.1002/cbic.201900346
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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 sphero
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Creators (Authors)
- Bruns, Dominique
- Gawehn, Erik
- Kumar, Karthiga Santhana
- Schneider, Petra
- Baumgartner, Martin
- Schneider, Gisbert
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Citations
Bruns, D., Gawehn, E., Kumar, K. S., Schneider, P., Baumgartner, M., & Schneider, G. (2020). Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning. Chembiochem, 21, 500–507. https://doi.org/10.1002/cbic.201900346
