9 citations · 16 across the 7 of their papers we have counts for
7 papers
Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
Haocheng Tang, Liang Shi, Ya-Shi Zhang +3
Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dynamics simulations and the scarcity of d…
Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics
Liang Shi, Jiarui Lu, Junqi Liu +3
Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations prov…
Aligning Protein Conformation Ensemble Generation with Physical Feedback
Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu +4
Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-consuming molecular dynamics (MD) simulati…
Structure Language Models for Protein Conformation Generation
Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu +4
Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Tradi…
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan +3
Protein function annotation is an important yet challenging task in biology. Recent deep learning advancements show significant potential for accurate function prediction by learni…
Structure-Informed Protein Language Model
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan +3
Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. However, traditional protein language model…
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Shengchao Liu, Chengpeng Wang, Jiarui Lu +5
Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained…