Mol-CycleGAN - a generative model for molecular optimization
This work addresses the problem of designing molecules with desired properties for drug development, representing an incremental improvement over existing methods.
The paper tackles the challenge of molecular optimization for drug development by introducing Mol-CycleGAN, a generative model that produces structurally similar molecules with optimized properties, significantly outperforming previous results in optimizing penalized logP for drug-like molecules.
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.