For all models but the Transformer, the first principal component is highly correlated with molecular weight (Fig. ), which makes sense for the RNN, AAE, and WAE, as all are length-dependent models. That the RNN-Attention model also exhibits this behavior demonstrates the need to commit fully to a Transformer model to avoid a significant component of the model's variance being devoted to sequence length. The comparative performance of the AAE with the Transformer shows, however, that it is not necessary to remove the length dependence to enforce ordering capability in the model, as long as more than the first and second PC are considered. In a more general sense, any model with a high-variance component built in as part of the architecture will likely demonstrate this behavior.