Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
Ziqiao Meng Peilin Zhao Yang Yu Irwin King
Organic reaction prediction is a critical task in drug discovery. Recently,
researchers have achieved non-autoregressive reaction prediction by modeling
the redistribution of electrons, resulting in state-of-the-art top-1 accuracy,
and enabling parallel sampling. However, the current non-autoregressive decoder
does not satisfy two essential rules of electron redistribution modeling
simultaneously: the electron-counting rule and the symmetry rule. This
violation of the physical constraints of chemical reactions impairs model
performance. In this work, we propose a new framework called that combines two
doubly stochastic self-attention mappings to obtain electron redistribution
predictions that follow both constraints. We further extend our solution to a
general multi-head attention mechanism with augmented constraints. To achieve
this, we apply Sinkhorn's algorithm to iteratively update self-attention
mappings, which imposes doubly conservative constraints as additional
informative priors on electron redistribution modeling. We theoretically
demonstrate that our can simultaneously satisfy both rules, which the current
decoder mechanism cannot do. Empirical results show that our approach
consistently improves the predictive performance of non-autoregressive models
and does not bring an unbearable additional computational cost.