There is increasing adoption of artificial intelligence in drug discovery. However, existing
studies use machine learning to mainly utilize the chemical structures of molecules but …

Molecular representation pretraining is critical in various applications for drug and material
discovery due to the limited number of labeled molecules, and most existing work focuses …

Recent advancements in conversational large language models (LLMs), such as ChatGPT,
have demonstrated remarkable promise in various domains, including drug discovery …

Generating 3D graphs of symmetry-group equivariance is of intriguing potential in broad
applications from machine vision to molecular discovery. Emerging approaches adopt …

Recent advancements in conversational large language models (LLMs), such as ChatGPT,
have demonstrated remarkable promise in various domains, including drug discovery …

Equivariance builds known symmetries into neural networks, often improving generalization.
However, equivariant networks cannot break self-symmetries present in any given input …

Few-shot anomaly detection aims to detect defects with only a limited number of normal
samples for training. Recent few-shot methods typically focus on object-level features rather …

Large language models (LLMs) are powerful instruction followers. However, many open-
ended generation tasks have a large “solution space” that depends on a user's needs. LLMs …