Generative Adversarial Networks (GANs): Engine and Applications

The latent layer consists of 5 neurons, one of which is responsible for GI (efficiency against cancer cells) and the four others are discriminated with normal distribution. So, a regression term was added to the Encoder cost function. Furthermore, the Encoder was restricted to map the same fingerprint to the same latent vector, independently from input concentration by additional manifold cost. After training, it is possible to generate molecules from a desired distribution and use a GI-neuron as a tuner of output compounds. Results of this work are the following: the trained AAE model predicted compounds that are already proven to be anticancer agents and new untested compounds that should be validated with experiments on anticancer activity.