  Single-step retrosynthesis is the cornerstone of retrosynthesis planning,
which is a crucial task for computer-aided drug discovery. The goal of
single-step retrosynthesis is to identify the possible reactants that lead to
the synthesis of the target product in one reaction. By representing organic
molecules as canonical strings, existing sequence-based retrosynthetic methods
treat the product-to-reactant retrosynthesis as a sequence-to-sequence
translation problem. However, most of them struggle to identify diverse
chemical reactions for a desired product due to the deterministic inference,
which contradicts the fact that many compounds can be synthesized through
various reaction types with different sets of reactants. In this work, we aim
to increase reaction diversity and generate various reactants using discrete
latent variables. We propose a novel sequence-based approach, namely
RetroDVCAE, which incorporates conditional variational autoencoders into
single-step retrosynthesis and associates discrete latent variables with the
generation process. Specifically, RetroDVCAE uses the Gumbel-Softmax
distribution to approximate the categorical distribution over potential
reactions and generates multiple sets of reactants with the variational
decoder. Experiments demonstrate that RetroDVCAE outperforms state-of-the-art
baselines on both benchmark dataset and homemade dataset. Both quantitative and
qualitative results show that RetroDVCAE can model the multi-modal distribution
over reaction types and produce diverse reactant candidates.
