  Reaction prediction has been recognized as a critical task in synthetic
chemistry, where the goal is to predict the outcome of a reaction based on the
given reactants. With the widespread adoption of generative models, the
Variational Autoencoder(VAE) framework has typically been employed to tackle
challenges in reaction prediction, where the reactants are encoded as a
condition for the decoder, which then generates the product. Despite
effectiveness, these conditional VAE (CVAE) models still fail to adequately
account for the inherent uncertainty in reaction prediction, which primarily
stems from the stochastic reaction process. The principal limitations are
twofold. Firstly, in these CVAE models, the prior is independent of the
reactants, leading to a default wide and assumed uniform distribution variance
of the generated product. Secondly, reactants with analogous molecular
representations are presumed to undergo similar electronic transition
processes, thereby producing similar products. This hinders the ability to
model diverse reaction mechanisms effectively. Since the variance in outcomes
is inherently non-uniform, we are thus motivated to develop a framework that
generates reaction products with non-uniform uncertainty. Firstly, we eliminate
the latent variable in previous CVAE models to mitigate uncontrol-label noise.
Instead, we introduce randomness into product generation via boosting to
ensemble diverse models and cover the range of potential outcomes, and through
dropout to secure models with minor variations. Additionally, we design a
ranking method to union the predictions from boosting and dropout, prioritizing
the most plausible products. Experimental results on the largest reaction
prediction benchmark USPTO-MIT show the superior performance of our proposed
method in modeling the non-uniform uncertainty compared to baselines.
