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 …

Abstract Large Language Models (LLMs) with strong abilities in natural language
processing tasks have emerged and have been applied in various kinds of areas such as …

Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

Toxicity prediction is a critical step in the drug discovery process that helps identify and
prioritize compounds with the greatest potential for safe and effective use in humans, while …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Given the increasing promise of graph neural networks (GNNs) in real-world applications,
several methods have been developed for explaining their predictions. Existing methods for …

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 …

Rationale is defined as a subset of input features that best explains or supports the
prediction by machine learning models. Rationale identification has improved the …