Table summarizes the evaluation of the effect of missing data in the predictions. More specifically, we present the evaluation metrics on four different partitions of the test-set depending on the number of the available spectra. We recall that the input to the model consists of four different spectra obtained through different specifications. However, not all molecules in the dataset have all four spectra available. Our results indicate that missing only one spectrum does not severely impact performance, but performance starts to degrade when less than three spectra are available.