  Chemical reaction practicality is the core task among all symbol intelligence
based chemical information processing, for example, it provides indispensable
clue for further automatic synthesis route inference. Considering that chemical
reactions have been represented in a language form, we propose a new solution
to generally judge the practicality of organic reaction without considering
complex quantum physical modeling or chemistry knowledge. While tackling the
practicality judgment as a machine learning task from positive and negative
(chemical reaction) samples, all existing studies have to carefully handle the
serious insufficiency issue on the negative samples. We propose an
auto-construction method to well solve the extensively existed long-term
difficulty. Experimental results show our model can effectively predict the
practicality of chemical reactions, which achieves a high accuracy of 99.76\%
on real large-scale chemical lab reaction practicality judgment.
