Unpaired Generative Molecule-to-Molecule Translation for Lead Optimization

Abstract

Molecular lead optimization is an important task of drug discovery focusing on generating novel molecules similar to a drug candidate but with enhanced properties. Prior works focused on supervised models requiring datasets of pairs of a molecule and an enhanced molecule. These approaches require large amounts of data and are limited by the bias of the specific examples of enhanced molecules. In this work, we present an unsupervised generative approach with a molecule-embedding component that maps a discrete representation of a molecule to a continuous space. The components are then coupled with a unique training architecture leveraging molecule fingerprints and applying double cycle constraints to enable both chemical resemblance to the original molecular lead while generating novel molecules with enhanced properties. We evaluate our method on multiple common molecular optimization tasks, including dopamine receptor (DRD2) and drug likeness (QED), and show our method outperforms previous state-of-the-art baselines. Moreover, we conduct thorough ablation experiments to show the effect and necessity of important components in our model. Furthermore, we demonstrate our method's ability to generate FDA-approved drugs it has never encountered before, such as Perazine and Clozapine, which are used to treat psychotic disorders, like Schizophrenia. The system is currently being deployed for use in the Targeted Drug Delivery and Personalized Medicine laboratories generating treatments using nanoparticle-based technology.