The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …

In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …

De novo drug design is a pivotal issue in pharmacology and a new area of focus in AI for
science research. A central challenge in this field is to generate molecules with specific …

Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play
a crucial role across various fields for the identification of molecular structures. A prevalent …

Generating desirable molecular structures in 3D is a fundamental problem for drug
discovery. Despite the considerable progress we have achieved, existing methods usually …

Attribute reconstruction is used to predict node or edge features in the pre-training of graph
neural networks. Given a large number of molecules, they learn to capture structural …

Many processes in biology and drug discovery involve various 3D interactions between
molecules, such as protein and protein, protein and small molecule, etc. Given that different …

Abstract Inductive relation prediction (IRP)---where entities can be different during training
and inference---has shown great power for completing evolving knowledge graphs. Existing …

Machine learning, particularly graph learning, is gaining increasing recognition for its
transformative impact across various fields. One such promising application is in the realm of …

In an era of digital ubiquity, efficient resource management and decision-making are
paramount across numerous industries. To this end, we present a comprehensive study on …