Fragment-based Sequential Translation for Molecular Optimization

Understand

Searching for novel molecular compounds with desired properties is an important problem in drug discovery.

• Many existing frameworks generate molecules one atom at a time.
• We instead propose a flexible editing paradigm that generates molecules using learned molecular fragments--meaningful substructures of molecules.
• To do so, we train a variational autoencoder (VAE) to encode molecular fragments in a coherent latent space, which we then utilize as a vocabulary for editing molecules to explore the complex chemical property space.