  Molecule-text modeling, which aims to facilitate molecule-relevant tasks with
a textual interface and textual knowledge, is an emerging research direction.
Beyond single molecules, studying reaction-text modeling holds promise for
helping the synthesis of new materials and drugs. However, previous works
mostly neglect reaction-text modeling: they primarily focus on modeling
individual molecule-text pairs or learning chemical reactions without texts in
context. Additionally, one key task of reaction-text modeling -- experimental
procedure prediction -- is less explored due to the absence of an open-source
dataset. The task is to predict step-by-step actions of conducting chemical
experiments and is crucial to automating chemical synthesis. To resolve the
challenges above, we propose a new pretraining method, ReactXT, for
reaction-text modeling, and a new dataset, OpenExp, for experimental procedure
prediction. Specifically, ReactXT features three types of input contexts to
incrementally pretrain LMs. Each of the three input contexts corresponds to a
pretraining task to improve the text-based understanding of either reactions or
single molecules. ReactXT demonstrates consistent improvements in experimental
procedure prediction and molecule captioning and offers competitive results in
retrosynthesis. Our code is available at https://github.com/syr-cn/ReactXT.
