Figure 1.

De novo AMP discovery with PepGAN. PepGAN receives sequence data consisting of AMPs and non-AMPs and creates new peptides with a generator and a discriminator. The generator samples a number of sequences stochastically. The reward for the sequences is evaluated by the discriminator and transmitted back to the generator to update the parameters. In normal GANs, the reward function represents fidelity, that is, how the sequences are similar to AMPs. In PepGAN, an activity predictor (shown in red) is incorporated in reward computation. Finally, top peptides are subject to experimental validation.