Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

As explanations are increasingly used to understand the behavior of graph neural networks
(GNNs), evaluating the quality and reliability of GNN explanations is crucial. However …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Learning effective molecular feature representation to facilitate molecular property prediction
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …

Although there has been considerable progress in molecular property prediction in
computer-aided drug design, there is a critical need to have fast and accurate models. Many …

Abstract Structure-based drug design (SBDD) aims to discover drug candidates by finding
molecules (ligands) that bind tightly to a disease-related protein (targets), which is the …

In offline model-based optimization, we strive to maximize a black-box objective function by
only leveraging a static dataset of designs and their scores. This problem setting arises in …

Streamlined data-driven drug discovery remains challenging, especially in resource-limited
settings. We present ZairaChem, an artificial intelligence (AI)-and machine learning (ML) …

The toxic effects of compounds on environment, humans, and other organisms have been a
major focus of many research areas, including drug discovery and ecological research …