6 papers
Exploring the Alignment of Generation and Understanding in Protein Structure Modeling
Junde Xu, Yuansheng Huang, Zijun Gao +5
Understanding and generation are often treated as two separate paradigms in training deep neural networks, despite the fact that both are trained with closely related objectives su…
An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining
Shi Li, Xujun Zhang, Mingquan Liu +5
Nucleic acids are increasingly recognized as therapeutic targets beyond conventional protein-centered drug discovery, yet accurate and efficient docking of small molecules to nucle…
AntigenLM: Structure-Aware DNA Language Modeling for Influenza
Yue Pei, Xuebin Chi, Yu Kang
Language models have advanced sequence analysis, yet DNA foundation models often lag behind task-specific methods for unclear reasons. We present AntigenLM, a generative DNA langua…
A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention
Qun Su, Kai Zhu, Qiaolin Gou +11
Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significa…
Graph Neural Networks in Modern AI-aided Drug Discovery
Odin Zhang, Haitao Lin, Xujun Zhang +9
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on m…
Descriptors-free Collective Variables From Geometric Graph Neural Networks
Jintu Zhang, Luigi Bonati, Enrico Trizio +4
Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables…