Recent methods for generating novel molecules use graph representations of
molecules and employ various forms of graph convolutional neural networks for
inference. However, training requires solving an expensive graph isomorphism
problem, which previous approaches do not address or solve only
approximately. In this work, we propose LF-MolGAN, a likelihood-free approach
for de novo molecule generation that avoids explicitly computing a
reconstruction loss. Our approach extends generative adversarial networks by
including an adversarial cycle-consistency loss to implicitly enforce the
reconstruction property. To capture properties unique to molecules, such as
valence, we extend Graph Isomorphism Network to multi-graphs. To quantify the
performance of models, we propose to compute the distance between
distributions of physicochemical properties with the 1-Wasserstein distance.
We demonstrate that LF-MolGAN more accurately learns the distribution over
the space of molecules than all baselines. Moreover, it can be utilized for
drug discovery by efficiently searching the space of molecules using
molecules’ continuous latent representation.