arXiv AI

Fold'EM: Direct atomic structure inference from Cryo-EM particles

arXiv AI

Jul 1

CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM

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

multimodal benchmarks

arXiv Machine Learning

Jun 2

CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps

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

diffusion benchmarks

arXiv Machine Learning

Jun 10

POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET

arXiv:2606. 10255v1 Announce Type: cross Abstract: Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cells, thereby linking molecular architecture to cellular organization in a native context.

By Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens, Zhuowen Zhao, Ariana Peck, Gus L. W. Hart, Grant J. Jensen, Bridget Carragher, Dari Kimanius

computer-vision benchmarks

arXiv Machine Learning

2d ago

Cryo-EM as a Stochastic Inverse Problem

arXiv:2509.05541v2 Announce Type: replace-cross Abstract: Cryo-electron microscopy (Cryo-EM) enables high-resolution imaging of biomolecules, but structural heterogeneity remains a major challenge in...

By Diego Sanchez Espinosa, Erik H Thiede, Yunan Yang

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

diffusion efficiency benchmarks safety

arXiv Machine Learning

Jul 31

APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems

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

benchmarks safety

arXiv AI

Jul 28

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

arXiv:2505. 12650v2 Announce Type: replace-cross Abstract: Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-forward models cannot verify physical validity.

By Yaotian Yang, Yiwen Tang, Yizhe Chen, Xiao Chen, Jiangjie Qiu, Hao Xiong, Haoyu Yin, Zhiyao Luo, Yifei Zhang, Sijia Tao, Wentao Li, Qinghua Zhang, Yuqiang Li, Wanli Ouyang, Bin Zhao, Xiaonan Wang, Fei Wei

agents multimodal benchmarks

arXiv Machine Learning

Jun 4

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.

By Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk

fine-tuning

arXiv Computer Vision

Sep 1

Reconstruction-Aware Cryo-EM Particle Picking

arXiv:2608.28838v1 Announce Type: cross Abstract: Cryo-electron microscopy (cryo-EM) determines the structures of proteins and macromolecular assemblies at near-atomic resolution, and the final 3D re...

By Riku Itsuji, Yuanhao Wang, Xingjian Li, Seonghui Min, Hideo Saito, Min Xu

fine-tuning benchmarks

arXiv Machine Learning

Jul 15

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.

By Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy

benchmarks

arXiv AI

Jul 7

Folding, Reasoning, and Scaling with Open-source Drug Discovery Engine

arXiv:2607. 03787v1 Announce Type: new Abstract: Accurately modeling biomolecular interactions is a central bottleneck in biology and therapeutic discovery.

By Aureka AI OpenDDE project

benchmarks

arXiv AI

Jun 19

Emyx: Fast and efficient all-atom protein generation

arXiv:2606. 19377v1 Announce Type: cross Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the underlying generative model.

By Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla, Constantin Schneider, Nicholas B. Woodall, Ruby Sedgwick, Christian D. Madsen, Andrew L. Hopkins, Edward O. Pyzer-Knapp

llms rag diffusion benchmarks

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