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Fig. 2 | Journal of Cheminformatics

Fig. 2

From: KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images

Fig. 2

Benchmarking the predictive power of ConvNet architectures on cytotoxicity data sets. Mean RMSE values (± standard deviation) on the test set across ten runs for each of the ConvNet architectures explored in this study (AlexNet [57], DenseNet-201 [58], ResNet152 [59] and VGG-19 [60]). Overall, all architectures enabled the generation of models with high predictive power on the test set, with RMSE values in the 0.65–0.96 pIC50 range. However, the extended versions of these architectures that we designed by including 5 fully-connected layers (see Fig. 1) constantly led to increased predictive power on the test set

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