Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology
Fig 2
Schematic representation of GAN-ZT architecture showing chemical structural input represented as weights (wi) and views (vi) matrices passed through two fully connected neural networks to produce a predicted toxicity matrix. Chemical features along with predicted or empirical toxicity matrices are then passed to a discriminator comprising a fully-connected neural network. Darker matrix shading indicates higher toxicity values.