Mol-CycleGAN - a generative model for molecular optimization

Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Michał Warchoł

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 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.

Keywords: generative adversarial networks, drug design, deep learning, molecule optimization

TL;DR: We introduce Mol-CycleGAN - a new generative model for optimization of molecules to augment drug design.

Code: [ ardigen/mol-cycle-gan](https://github.com/ardigen/mol-cycle-gan)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1902.02119/code)