  Retrosynthesis is a fundamental but challenging task in organic chemistry,
with broad applications in fields such as drug design and synthesis. Given a
target molecule, the goal of retrosynthesis is to find out a series of
reactions which could be assembled into a synthetic route which starts from
purchasable molecules and ends at the target molecule. The uncertainty of
reactions used in retrosynthetic planning, which is caused by hallucinations of
backward models, has recently been noticed. In this paper we propose a succinct
probabilistic model to describe such uncertainty. Based on the model, we
propose a new retrosynthesis planning algorithm called retro-prob to maximize
the successful synthesis probability of target molecules, which acquires high
efficiency by utilizing the chain rule of derivatives. Experiments on the
Paroutes benchmark show that retro-prob outperforms previous algorithms, retro*
and retro-fallback, both in speed and in the quality of synthesis plans.
