Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

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 …

The clinical efficacy and safety of a drug is determined by its molecular properties and
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …

Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …

The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …

Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to
explain the predictions made by GNNs. Existing explanation methods mainly focus on post …

This paper studies few-shot molecular property prediction, which is a fundamental problem
in cheminformatics and drug discovery. More recently, graph neural network based model …

Deep learning has been a prevalence in computational chemistry and widely implemented
in molecular property predictions. Recently, self-supervised learning (SSL), especially …

Generative pre-trained Transformer (GPT) has demonstrates its great success in natural
language processing and related techniques have been adapted into molecular modeling …

In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Unlike the unified pre-training strategy employed in the language field, the graph field …