Antimicrobial peptides (AMPs) are promising candidates to combat multidrug‐resistant
pathogens. However, the high cost of extensive wet‐lab screening has made AI methods for …

The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding properties …

Equivariant Graph Neural Networks (GNNs) have made remarkable success in a variety of
scientific applications. However, existing equivariant GNNs encounter the efficiency issue for …

Crystalline materials, with their symmetrical and periodic structures, possess a diverse array
of properties and have been widely used in various fields, ranging from electronic devices to …

In computational molecular and materials science, determining equilibrium structures is the
crucial first step for accurate subsequent property calculations. However, the recent …

In modern computational materials, machine learning has shown the capability to predict
interatomic potentials, thereby supporting and accelerating conventional molecular …

Graph neural networks (GNNs) have emerged as powerful tools for quantum chemical
property prediction, leveraging the inherent graph structure of molecular systems. GNNs …

Graph neural network (GNN) architectures have emerged as promising force field models,
exhibiting high accuracy in predicting complex energies and forces based on atomic …

Predicting quantum operator matrices such as Hamiltonian, overlap, and density matrices in
the density functional theory (DFT) framework is crucial for material science. Current …

Accurately identifying sites of metabolism (SoM) mediated by cytochrome P450 (CYP)
enzymes, which are responsible for drug metabolism in the body, is critical in the early stage …