Computational approaches have revolutionized the field of drug discovery, collectively
known as Computer-Assisted Drug Design (CADD). Advancements in computing power …

In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …

Heterogeneous graph neural networks (HGNNs) were proposed for representation learning
on structural data with multiple types of nodes and edges. To deal with the performance …

Molecular property prediction is an important task in drug discovery. However, experimental
data for many drug molecules are limited, especially for novel molecular structures or rare …

The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Molecular property prediction holds significant importance in drug discovery, enabling the
identification of biologically active compounds with favorable drug-like properties. However …

Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both
academia and industry because prevailing studies on heterogeneous graphs often suffer …

Molecular property prediction (MPP) is integral to drug discovery and material science, but
often faces the challenge of data scarcity in real-world scenarios. Addressing this, few-shot …

Few-Shot Molecular Property Prediction (FSMPP) is an improtant task on drug discovery,
which aims to learn transferable knowledge from base property prediction tasks with …