Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets.

Protein inverse folding has attracted increasing attention in recent years.

Table 3: Comparison of models trained with different representations of protein structure across various tasks, on a random data split . The optimal choice of representation depends on the task. Shown are mean and standard deviation across four runs with different seeds. Table 4: Comparison of models trained with different representations of protein structure across various tasks, on a sequence data split . Table 5: Comparison of models trained with different representations of protein structure across various tasks, on a structure data split .

However, the heterogeneity of protein data constitutes a problem for reproducible machine learning.

Code will be released upon acceptance.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/