Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …

Inverse protein folding is challenging due to its inherent one-to-many map**
characteristic, where numerous possible amino acid sequences can fold into a single …

This paper demonstrates that language models are strong structure-based protein
designers. We present LM-Design, a generic approach to reprogramming sequence-based …

Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …

Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs)
for molecules. Typically, atom types as node attributes are randomly masked and GNNs are …

Diffusion probabilistic models have made their way into a number of high-profile
applications since their inception. In particular, there has been a wave of research into using …

In recent years, AI-assisted drug design methods have been proposed to generate
molecules given the pockets' structures of target proteins. Most of them are {\em atom-level …

While DeepMind has tentatively solved protein folding, its inverse problem--protein design
which predicts protein sequences from their 3D structures--still faces significant challenges …

Protein representation learning is a challenging task that aims to capture the structure and
function of proteins from their amino acid sequences. Previous methods largely ignored the …

Recent studies have shown competitive performance in protein inverse folding, while most
of them disregard the importance of predictive confidence, fail to cover the vast protein …