Conditional molecular design with deep generative models
This addresses the problem of efficient molecular design for drug discovery, representing an incremental improvement over existing deep generative approaches.
The paper tackles the challenge of efficiently exploring large chemical spaces for molecular design by presenting a conditional molecular generation method that simultaneously performs property prediction and molecule generation using a semi-supervised variational autoencoder. The model improves property prediction by leveraging unlabeled molecules and generates novel drug-like molecules that fulfill various target conditions.
Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semi-supervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by sampling from the generative distribution estimated by the model. We demonstrate the effectiveness of the proposed model by evaluating it on drug-like molecules. The model improves the performance of property prediction by exploiting unlabeled molecules, and efficiently generates novel molecules fulfilling various target conditions.