arXiv:2606. 31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.
By Minzhang Li, Mingrui Li, Weichen Qin, Qihe Chen, Sixian Shen, Yuan Pei, Jiakai Zhang, Jingyi Yu
arXiv:2606. 00955v1 Announce Type: new Abstract: Despite the growing availability of cryo-electron microscopy (cryo-EM) density maps, effectively leveraging them for protein representation remains challenging.
By Dan Luo, Xuan Lin, Peng Zhou, Junwen Zhu, Tengfei Ma, Xiangxiang Zeng, Yiping Liu
arXiv:2607. 28553v1 Announce Type: new Abstract: Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery.
By Shentong Mo, Yatao Bian
Fold'EM is an inference-time framework that directly combines protein generative model priors with cryo‑EM particle images to recover atomic structures, bypassing the traditional density reconstruction step. It can determine structures from a small number of particles, jointly infer orientations in an ab‑initio setting, and resolve distinct conformational states in heterogeneous samples without separate map reconstruction. The method demonstrates accurate atomic models on both synthetic and experimental datasets, showing promise for low‑sample and low‑population conformational analysis.
By Advaith Maddipatla, M\"art-Erik M\"aeots, Marco Pegoraro, Nikolaus Dr\"ager, Roberto Covino, Sanketh Vedula, Martin Pacesa, Alex M. Bronstein
Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to generalize across downstream tasks. While deep learni...
arXiv:2610.08400v1 Announce Type: cross
Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to ge...
By Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, Fran\c{c}ois R J Cornet, Mikkel Jordahn, Mikkel N. Schmidt