Protein Structure Representation Learning by Geometric Pretraining. (arXiv:2203.06125v1 [cs.LG])

Learning effective protein representations is critical in a variety of tasks
in biology such as predicting protein function or structure. Existing
approaches usually pretrain protein language models on a large number of
unlabeled amino acid sequences and then finetune the models with some labeled
data in downstream tasks. Despite the effectiveness of sequence-based
approaches, the power of pretraining on smaller numbers of known protein
structures has not been explored for protein property prediction, though
protein structures are known to be …

arxiv learning protein structure representation representation learning