Genomic sequences are traditionally represented as strings of characters: A (adenine), C
(cytosine), G (guanine), and T (thymine). However, an alternative approach involves …

Molecular relational learning (MRL) is crucial for understanding the interaction behaviors
between molecular pairs, a critical aspect of drug discovery and development. However, the …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …

Machine learning (ML) systems have enabled the modelling of quantitative structure–
property relationships (QSPR) and structure-activity relationships (QSAR) using existing …

Large Language Models (LLMs) with their strong task-handling capabilities have shown
remarkable advancements across a spectrum of fields, moving beyond natural language …

Molecule-and-text cross-modal representation learning has emerged as a promising
direction for enhancing the quality of molecular representation, thereby improving …

The production of expressive molecular representations with scarce labeled data is
challenging for AI-driven drug discovery. Mainstream studies often follow a pipeline that pre …

Enzyme design plays a crucial role in both industrial production and biology. However, this
field faces challenges due to the lack of comprehensive benchmarks and the complexity of …

Molecule-and-text cross-modal representation learning has emerged as a promising
direction for enhancing the quality of molecular representation, thereby improving …

Recent research in molecular discovery has primarily been devoted to small, drug-like
molecules, leaving many similarly important applications in material design without …