arXiv Machine Learning

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.

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 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

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

Jun 3

Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding

arXiv:2606. 02629v1 Announce Type: cross Abstract: Protein-protein interactions (PPIs) are essential for many biological processes.

By Zaifei Yang, Samuel Ping-Man Choi, James Kwok

rag multimodal benchmarks

arXiv Machine Learning

Jul 22

GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks

arXiv:2607. 19083v1 Announce Type: new Abstract: Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific tasks, outputs, and training regimes.

By Daniele Angioletti, Marco Nobile, Vittorio Limongelli

diffusion benchmarks

arXiv Machine Learning

Jun 24

3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy

arXiv:2606. 23964v1 Announce Type: new Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells.

By Amirhossein Kardoost, Lion Gleiter, Tingying Peng, Carsten Marr

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 16

Circuit Tracing in Autoregressive Protein Language Models

arXiv:2606. 16044v1 Announce Type: new Abstract: Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood.

By Darin Tsui, William Deinzer, Daniel Saeedi, Amirali Aghazadeh

llms safety

arXiv Machine Learning

Jun 16

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders

arXiv:2606. 14999v1 Announce Type: new Abstract: Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them.

By Monika Choudhary, Xiaoya Chong, Runbo Jiang, Wiebke Koepp, Petrus H. Zwart, Damon English, Gregory M. Su, Eric Schaible, Chenhui Zhu, Mostafa Nassr, Noah P. Wamble, Kelvin Kam-Yun Li, Jonathan M. Chan, Jose Carlos Diaz, Cameron McKay, Lynn Katz, Benny Freeman, Guillaume Freychet, Yevgen Matviychuk, Eliot Gann, Daniel B. Allan, Benedikt Sochor, Frank Schluenzen, Stephan V. Roth, Ethan Crumlin, Dylan McReynolds, Tanny Chavez, Alexander Hexemer

diffusion benchmarks

arXiv Machine Learning

Jul 15

SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems

arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.

By Yuxuan Ren, Fan Yang, Jianhua Yao, Yatao Bian

llms diffusion benchmarks

arXiv Machine Learning

Jun 2

Latent Diffusion Pretraining for Crystal Property Prediction

arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.

By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly

llms diffusion fine-tuning

arXiv Machine Learning

Jul 21

ChemFusion: A Multimodal Cross-Attention Network for Reaction Yield Prediction

arXiv:2607. 17033v1 Announce Type: new Abstract: Forecasting the outcomes of transition-metal-catalyzed reactions is notoriously complex due to the interplay of diverse physical and chemical variables.

By Qiwei Han, Chi Zhou

multimodal safety

arXiv Machine Learning

Jul 1

Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?

arXiv:2606. 31126v1 Announce Type: new Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design.

By Davy Guan, Lu Zhang, Asiri Wijesinghe, Allen Zhu, He Zhao, Helen Power, F. Hafna Ahmed, Andrew Warden, Cheng Soon Ong, Daniel M. Steinberg

llms safety

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