  De novo drug design requires simultaneously generating novel molecules
outside of training data and predicting their target properties, making it a
hard task for generative models. To address this, we propose Joint Transformer
that combines a Transformer decoder, Transformer encoder, and a predictor in a
joint generative model with shared weights. We formulate a probabilistic
black-box optimization algorithm that employs Joint Transformer to generate
novel molecules with improved target properties and outperforms other
SMILES-based optimization methods in de novo drug design.
