Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

As graph neural networks are becoming more and more powerful and useful in the field of
drug discovery, many pharmaceutical companies are getting interested in utilizing these …

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

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 …

One-shot image segmentation aims to undertake the segmentation task of a novel class with
only one training image available. The difficulty lies in that image segmentation has …

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 …