One problem with the models discussed above is that the accuracy for the entire chemical context is low compared to the accuracy for the individual specifies, a natural effect of modeling the different species individually. To the best of our knowledge, no one has developed a model that jointly predicts the full reaction condition context, i.e., a specific set composed of a catalyst, a solvent, and reagents, which are treated as single label during modeling. Although the methods mentioned above attempt to couple predictions of individual conditions in various ways, the predictions of different conditions are essentially independent. Instead, modeling the full chemical context would have the advantage of circumventing the error propagation problem plaguing earlier proposed methods. However, because different reaction classes use different sets of chemical contexts, creating a general-purpose model applicable to any reaction is challenging. Furthermore, existing combinations of conditions would naturally limit the scope of the model and hinder the prediction of novel contexts.