In this approach, all reaction data within a single model were generated under the same set of conditions, thus eliminating reaction contexts such as condensation reagents, catalysts, bases, and solvents. This allowed the model to focus solely on the relationship between substrates and products, leading to improved learning and predictive accuracy. However, a significant challenge with this method is the potential loss of critical reaction condition information. Since reaction conditions play a crucial role in determining the outcome of chemical reactions, ignoring them can lead to incomplete models that do not accurately reflect real-world scenarios. To address this issue, we incorporated intermediate information based on reaction mechanisms into our model.