arXiv Machine Learning By Kaihui Cheng, Zhiqiang Cai, Peng Tu, Yisong Yao, Limei Han, Libo Wu, Siyu Zhu, Tzuhsiung Yang, Yuan Qi

DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales

Read the original on arXiv Machine Learning →

arXiv:2607. 15309v1 Announce Type: cross Abstract: Proteins function through coordinated motion across multiple spatial and temporal scales, underpinning processes such as ligand binding, allostery, and catalysis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 16

Learning Topological Representations for Molecular Dynamics

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt
arXiv Machine Learning
Jun 9

Few-step Cofolding with All-Atom Flow Maps

arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.

By Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam, Pranav Murugan, Rafael G\'omez-Bombarelli, Tommi Jaakola, Maruan Al-Shedivat, Nicholas Matthew Boffi, Avishek Joey Bose
arXiv Machine Learning
Jul 28

Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

arXiv:2607. 23518v1 Announce Type: new Abstract: The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination.

By Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang, Yunhong Lu, Chenxi Cai, Yan Zhang, Min Zhang