Deep learning architectures have proved versatile in a number of drug discovery
applications, including the modeling of in vitro compound activity. While controlling for …

The application of convolutional neural networks (ConvNets) to harness high-content
screening images or 2D compound representations is gaining increasing attention in drug …

While the use of deep learning in drug discovery is gaining increasing attention, the lack of
methods to compute reliable errors in prediction for Neural Networks prevents their …

The versatility of similarity searching and quantitative structure–activity relationships to
model the activity of compound sets within given bioactivity ranges (ie, interpolation) is well …