Another observation is that the generalization to molecular weight reverse splitting (i.e., training on larger-weight molecules and testing on smaller ones) posed a harder challenge to both types of models compared to molecular weight splitting (i.e., training on smaller molecules and testing on larger ones). This indicates that models can have a more accurate prediction and higher generalization capacity when tested on molecules larger than those they were trained on, compared to the case when they are tested on molecules smaller than those they were trained on.