# Mol-CycleGAN - a generative model for molecular optimization

## TL;DR

Mol-CycleGAN is a novel generative model that optimizes molecular properties while maintaining structural similarity, significantly improving drug-like molecule property optimization compared to previous methods.

## Contribution

Introduces Mol-CycleGAN, a CycleGAN-based model for molecular optimization that preserves structural similarity while enhancing desired properties.

## Key findings

- Outperforms previous models in penalized logP optimization
- Effectively optimizes structural properties like halogen presence and aromatic rings
- Generates molecules with high structural similarity to original compounds

## Abstract

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.