Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

The integration of biomolecular modeling with natural language (BL) has emerged as a
promising interdisciplinary area at the intersection of artificial intelligence, chemistry and …

Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

Language Models (LMs) excel in understanding textual descriptions of proteins, as evident
in biomedical question-answering tasks. However, their capability falters with raw protein …

Credit card fraud poses a significant threat to the economy. While Graph Neural Network
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …

Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual
interface and textual knowledge, is an emerging research direction. Beyond single …

Despite widespread applications of knowledge graphs (KGs) in various tasks such as
question answering and intelligent conversational systems, existing KGs face two major …

Despite Graph Neural Networks (GNNs) demonstrating considerable promise in graph
representation learning tasks, GNNs predominantly face significant issues with overfitting …

Recent advances in large language models (LLMs) like ChatGPT have shed light on the
development of knowledgeable and versatile AI research assistants in various scientific …

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …