  Designing a molecule with desired properties is one of the biggest challenges
in drug development, as it requires optimization of chemical compound
structures with respect to many complex properties. To augment the compound
design process we introduce Mol-CycleGAN - a CycleGAN-based model that
generates optimized compounds with high structural similarity to the original
ones. Namely, given a molecule our model generates a structurally similar one
with an optimized value of the considered property. We evaluate the performance
of the model on selected optimization objectives related to structural
properties (presence of halogen groups, number of aromatic rings) and to a
physicochemical property (penalized logP). In the task of optimization of
penalized logP of drug-like molecules our model significantly outperforms
previous results.
