  Recent advances in deep learning have accelerated its use in various
applications, such as cellular image analysis and molecular discovery. In
molecular discovery, a generative adversarial network (GAN), which comprises a
discriminator to distinguish generated molecules from existing molecules and a
generator to generate new molecules, is one of the premier technologies due to
its ability to learn from a large molecular data set efficiently and generate
novel molecules that preserve similar properties. However, different
pharmaceutical companies may be unwilling or unable to share their local data
sets due to the geo-distributed and sensitive nature of molecular data sets,
making it impossible to train GANs in a centralized manner. In this paper, we
propose a Graph convolutional network in Generative Adversarial Networks via
Federated learning (GraphGANFed) framework, which integrates graph
convolutional neural Network (GCN), GAN, and federated learning (FL) as a whole
system to generate novel molecules without sharing local data sets. In
GraphGANFed, the discriminator is implemented as a GCN to better capture
features from molecules represented as molecular graphs, and FL is used to
train both the discriminator and generator in a distributive manner to preserve
data privacy. Extensive simulations are conducted based on the three bench-mark
data sets to demonstrate the feasibility and effectiveness of GraphGANFed. The
molecules generated by GraphGANFed can achieve high novelty (=100) and
diversity (> 0.9). The simulation results also indicate that 1) a lower
complexity discriminator model can better avoid mode collapse for a smaller
data set, 2) there is a tradeoff among different evaluation metrics, and 3)
having the right dropout ratio of the generator and discriminator can avoid
mode collapse.
