  Current drug discovery is expensive and time-consuming. It remains a
challenging task to create a wide variety of novel compounds with desirable
pharmacological properties and cheaply available to low-income people. In this
work, we develop a generative network complex (GNC) to generate new drug-like
molecules based on the multi-property optimization via the gradient descent in
the latent space of an autoencoder. In our GNC, both multiple chemical
properties and similarity scores are optimized to generate and predict
drug-like molecules with desired chemical properties. To further validate the
reliability of the predictions, these molecules are reevaluated and screened by
independent 2D fingerprint-based predictors to come up with a few hundreds of
new drug candidates. As a demonstration, we apply our GNC to generate a large
number of new BACE1 inhibitors, as well as thousands of novel alternative drug
candidates for eight existing market drugs, including Ceritinib, Ribociclib,
Acalabrutinib, Idelalisib, Dabrafenib, Macimorelin, Enzalutamide, and
Panobinostat.
