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 a chemical scaffold of interest. 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.
We introduce Mol-CycleGAN - a new generative model for optimization of molecules to augment drug design. The paper presents an approach for optimizing molecular properties based on the application of CycleGANs to variational autoencoders for molecules and employs a domain-specific VAE called Junction Tree VAE (JT-VAE). This paper uses a variational autoencoders to learn a translation function, from the set of molecules without the interested property to the set of molecules with the property. 