  The application of convolutional neural networks (ConvNets) to harness
high-content screening images or 2D compound representations is gaining
increasing attention in drug discovery. However, existing applications often
require large data sets for training, or sophisticated pretraining schemes.
Here, we show using 33 IC50 data sets from ChEMBL 23 that the in vitro activity
of compounds on cancer cell lines and protein targets can be accurately
predicted on a continuous scale from their Kekule structure representations
alone by extending existing architectures, which were pretrained on unrelated
image data sets. We show that the predictive power of the generated models is
comparable to that of Random Forest (RF) models and fully-connected Deep Neural
Networks trained on circular (Morgan) fingerprints. Notably, including
additional fully-connected layers further increases the predictive power of the
ConvNets by up to 10%. Analysis of the predictions generated by RF models and
ConvNets shows that by simply averaging the output of the RF models and
ConvNets we obtain significantly lower errors in prediction for multiple data
sets, although the effect size is small, than those obtained with either model
alone, indicating that the features extracted by the convolutional layers of
the ConvNets provide complementary predictive signal to Morgan fingerprints.
Lastly, we show that multi-task ConvNets trained on compound images permit to
model COX isoform selectivity on a continuous scale with errors in prediction
comparable to the uncertainty of the data. Overall, in this work we present a
set of ConvNet architectures for the prediction of compound activity from their
Kekule structure representations with state-of-the-art performance, that
require no generation of compound descriptors or use of sophisticated image
processing techniques.
