776 citations · 1.4k across the 18 of their papers we have counts for
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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…
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…
Design of Ligand-Binding Proteins with Atomic Flow Matching
Junqi Liu, Shaoning Li, Chence Shi +2
Designing novel proteins that bind to small molecules is a long-standing challenge in computational biology, with applications in developing catalysts, biosensors, and more. Curren…
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
Jiarui Lu, Zuobai Zhang, Bozitao Zhong +2
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations…
Protein Sequence and Structure Co-Design with Equivariant Translation
Chence Shi, Chuanrui Wang, Jiarui Lu +2
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and desired functions has been a long-standi…
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang, Huiyu Cai, Chence Shi +2
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, wher…
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Yutong Xie, Chence Shi, Hao Zhou +4
Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing neural models to generate either molecular sequence…