Chemical reactivity models are developed to predict chemical reaction
outcomes in the form of classification (success/failure) or regression (product
yield) tasks. The vast majority of the reported models are trained solely on
chemical information such as reactants, products, reagents, and solvents, but
not on the details of a synthetic protocol. Herein incorporation of procedural
text with the aim to augment the Graphormer reactivity model and improve its
accuracy is presented. Two major approaches are used: training an adapter
Graphormer model that is provided with a GPT-2-derived latent representation of
the text procedure (ReacLLaMA-Adapter) and labeling an unlabeled part of a
dataset with the LLaMA 2 model followed by training the Graphormer on an
extended dataset (Zero-Shot Labeling ReacLLaMA). Both methodologies enhance the
discernment of unpromising reactions, thereby providing more accurate models
with improved specificity.

通过结合过程文本并使用两种主要方法（ReacLLaMA-Adapter 和 Zero-Shot Labeling ReacLLaMA），改进 Graphormer 反应模型的准确性，提高了对不良反应的辨别能力，从而提供了更准确的具有改进特异性的化学反应模型。