We consider representation learning for proteins with 3D structures. We build 3D graphs
based on protein structures and develop graph networks to learn their representations …

Equivariance has gained strong interest as a desirable network property that inherently
ensures robust generalization. However, when dealing with complex systems such as …

Innovations like protein diffusion have enabled significant progress in de novo protein
design, which is a vital topic in life science. These methods typically depend on protein …

We present a deep-learning-based platform, MIND-S, for protein post-translational
modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural …

Proteins are fundamental biological entities that play a key role in life activities. The amino
acid sequences of proteins can be folded into stable 3D structures in the real …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

Accurate prediction of protein–ligand binding affinities can significantly advance the
development of drug discovery. Several graph neural network (GNN)-based methods learn …

This work tackles the issue of directed evolution in computational protein design that makes
accurate predictions of the function of a protein mutant. We design a lightweight pre-training …

Classical approaches for flood prediction apply numerical methods for the solution of partial
differential equations that capture the physics of inundation processes (eg, the 2D Shallow …