5 accepted papers
A significant portion of disease-relevant proteins remain undruggable due to shallow, flexible, or otherwise ill-defined binding pockets that hinder conventional molecule screening. Fragment-based drug discovery (FBDD) offers a promising alternative, as small, low-complexity fragments can flexibly e…
Protein-ligand binding prediction is central to virtual screening and affinity ranking, two fundamental tasks in drug discovery. While recent retrieval-based methods embed ligands and protein pockets into Euclidean space for similarity-based search, the geometry of Euclidean embeddings often fails t
Large language models (LLMs) can output sensitive information, which has emerged as a novel safety concern. Previous works focus on structured sensitive information (e.g. personal identifiable information). However, we notice that sensitive information can also be at semantic level, i.e. semantic s…
Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods—whether physics-based or deep learning-based—are developed around *holo* protein structures with known ligand-bound pockets. Consequently, their performance degrades significantly on *apo* or predicted…
Flexible docking, which predicts the binding conformations of both proteins and small molecules by modeling their structural flexibility, plays a vital role in structure-based drug design. Although recent generative approaches, particularly diffusion-based models, have shown promising results, they…