Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

Graph neural networks (GNN) have been shown to provide substantial performance
improvements for atomistic material representation and modeling compared with descriptor …

The global COVID‐19 (coronavirus disease 2019) pandemic, which was caused by the
severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has resulted in a …

We propose a novel deep learning approach for predicting drug–target interaction using a
graph neural network. We introduce a distance-aware graph attention algorithm to …

De novo drug design is a stationary way to build novel ligands in the confined pocket of
receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation …

Modern therapeutic development often involves several stages that are interconnected, and
multiple iterations are usually required to bring a new drug to the market. Computational …

Despite their enormous potential, machine learning methods have only found limited
application in predicting reaction outcomes, because current models are often highly …

Modern computational approaches and machine learning techniques accelerate the
invention of new drugs. Generative models can discover novel molecular structures within …