Molecular generative model based on conditional variational autoencoder for de novo molecular design
This work addresses the challenge of designing new molecules with specific properties for drug discovery, though it appears incremental as it builds on existing conditional variational autoencoder methods.
The authors tackled the problem of de novo molecular design by proposing a conditional variational autoencoder model that controls multiple molecular properties simultaneously, demonstrating it can generate drug-like molecules with five target properties and adjust single properties independently.
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.