ReacLLaMA: Merging chemical and textual information in chemical
  reactivity AI models
Aline Hartgers Ramil Nugmanov Kostiantyn Chernichenko Joerg Kurt Wegner
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.