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. The principal goal of the drug design process is to find new chemical compounds that are able to modulate the activity of a given target (typically a protein) in a desired way BID27 ). However, finding such molecules in high-dimensional chemical space of all molecules without any prior knowledge is nearly impossible. In silico methods have been introduced to leverage the existing chemical, pharmacological and biological knowledge, thus forming a new branch of science -computer-aided drug design (CADD) BID26 BID0 . In particular, the recent advancements in deep learning encouraged its application to CADD BID4 . Computer methods are nowadays applied at every stage of drug design pipelines BID26 from the search of new, potentially active compounds BID22 , through optimization of their activity and physicochemical profile BID15 and simulating their scheme of interaction with the target protein BID9 , to assisting in planning the synthesis and evaluation of its difficulty BID30 .In the center of our interest are the hit-to-lead and lead optimization phases of the compound design process. Their goals are to optimize drug-like molecules identified in previous steps in terms of, respectively, the desired activity profile (increased potency towards given target protein and provision of inactivity towards undesired proteins) and the physicochemical and pharmacokinetic properties. The challenge here is to optimize a molecule with respect to multiple properties simultaneously BID15 .Our principal contribution is the introduction of Mol-CycleGAN, a generative model based on CycleGAN BID36 with the goal to augment the compound design process. We show that our model can generate molecules that possess desired properties 1 while retaining their chemical scaffolds. Given a starting molecule, the model generates a similar one but with a desired characteristics. The similarity between the two molecules is important in the context of multiparameter optimization, as it makes it easier to optimize the selected property without spoiling the previously optimized ones. To the best of our knowledge , this is the first approach to molecule generation that uses the CycleGAN architecture. We evaluate our model on its ability to perform structural transformations and molecular optimization. The former indicates that the model is able to do simple structural modifications such as a change in the presence of halogen groups or number of aromatic rings. In the latter, we aim to maximize penalized logP to assess the model's utility for compound design. Penalized logP is a physicochemical property often selected as a testing ground for molecule optimization models BID18 BID35 , as it is relevant in the drug design process. In the optimization of penalized logP for drug-like molecules our model significantly outperforms previous results. In this work, we introduced Mol-CycleGAN -a new model based on CycleGAN that can be used for the de novo generation of molecules. The advantage of the proposed model is the ability to learn transformation rules from the sets of compounds with desired and undesired values of the considered property. The model operates in the latent space trained by another model -in our work we use the latent space of JT-VAE. The model can generate molecules with desired properties -both structural and physicochemical. The generated molecules are close to the starting ones and the degree of similarity can be controlled via a hyperparameter. In the task of constrained optimization of druglike molecules our model significantly outperforms previous results. In future work we will extend the approach to multi-parameter optimization of molecules using StarGAN BID5 . It would also be interesting to test the model on cases where a small structural change leads to a drastic change in the property (e.g. on the so-called activity cliffs), which are hard for other approaches. Another interesting direction is the application of the model to working on text embeddings, where the X and Y sets could be characterized, e.g., by different sentiment.
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. 