Conditional Molecule Generation Using Transformer Decoder

Abstract

Deep learning is being widely used for de novo generation of molecules. Molecules can be represented in the form of string of characters, SMILES representation, which allows the implementation of transformer architectures. In this work, we propose a transformer decoder based network for the generation of molecules with high validity, uniqueness and novelty. The proposed model is capable of conditional generation where the condition can be based on a scaffold or/and multiple physicochemical properties. Moreover, we show that saliency maps can be used to make the generative process interpretable.