most citedPharmAgents: Building a Virtual Pharma with Large Language Model Agents

2 citations · 2 across the 3 of their papers we have counts for

collaborators

9 papers

cs.LG2025

SDrug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening

Bowei He, Bowen Gao, Yankai Chen +5

Virtual screening (VS) is an essential task in drug discovery, focusing on the identification of small-molecule ligands that bind to specific protein pockets. Existing deep learnin…

cs.LG2025

Coder as Editor: Code-driven Interpretable Molecular Optimization

Wenyu Zhu, Chengzhu Li, Xiaohe Tian +7

Molecular optimization is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in…

cs.LG2025

Learning Protein-Ligand Binding in Hyperbolic Space

Jianhui Wang, Wenyu Zhu, Bowen Gao +4

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…

physics.chem-ph2025

Revisiting Sampling Strategies for Molecular Generation

Yuyan Ni, Shikun Feng, Wei-Ying Ma +2

Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods…

cs.LG2025

AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation

Wenyu Zhu, Jianhui Wang, Bowen Gao +5

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…

cs.LG2025

Manipulating 3D Molecules in a Fixed-Dimensional E(3)-Equivariant Latent Space

Zitao Chen, Yinjun Jia, Zitong Tian +2

Medicinal chemists often optimize drugs considering their 3D structures and designing structurally distinct molecules that retain key features, such as shapes, pharmacophores, or c…