How I Built a Generative AI Model that can Generate Novel Small Molecules for Drug Discovery! — Part 3: Putting it all Together in a WGAN

Here, we just extend <code>keras.Model</code> and initialize our WGAN with a generator, discriminator, and related parameters. <ul> <li><code>generator</code> and <code>discriminator</code> are the models we defined earlier to use in our GAN.</li> <li><code>discriminator_steps</code> and <code>generator_steps</code> represent the number of steps to train the discriminator and generator in each iteration.</li> <li><code>gp_weight</code> is the weight assigned to the gradient penalty term.</li> </ul> <a href="https://medium.com/@himynameisaftab/how-i-built-a-generative-ai-model-that-can-generate-novel-small-molecules-for-drug-discovery-5045002b8b70">Website</a>