A Novel de novo Drug Design Approach using Deep Generative Model

Abstract

A major challenge in chemoinformatics is to generate novel structures with desirable activity. The structure generation method based on deep generative model has the advantage in obtaining novel structures via SMILES representation without exhaustive structure generation. However, some studies use a large amount of data to model the relationship between chemical structures and their activity. Here we propose a new deep generative model combined with the semi-supervised learning, which enables the prediction from small size datasets. We conducted a case study to confirm the predictive ability for the alpha2A adrenergic receptor (ADRA2A) dataset, and showed that the proposed model performed better than the previous methods. We plan to further develop and verify our model so that it generates structures with desirable activity from small size datasets.