arXiv Machine Learning June 29, 2026 By Giosue Migliorini, Aristofanis Rontogiannis, Grigori Guitchounts, Nicholas Franklin, Axel Elaldi, Olivia Viessmann

PairSAE: Mechanistic Interpretability from Pair Representations in Protein Co-Folding

Read the original on arXiv Machine Learning →

arXiv:2606. 27440v1 Announce Type: new Abstract: Foundation models for structural biology have achieved remarkable performance in predicting biomolecular structure and show promise for the design of proteins and small molecules.

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

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

Jun 25

Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks

arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?

By Kevin Lu, Jannik Brinkmann, Stefan Huber, Aaron Mueller, Yonatan Belinkov, David Bau, Chris Wendler

arXiv Machine Learning

Jun 18

KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction

arXiv:2506. 13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery.

By Han Liu, Keyan Ding, Peilin Chen, Yinwei Wei, Liqiang Nie, Dapeng Wu, Shiqi Wang

rag benchmarks safety

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

Aug 12

BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning

arXiv:2604. 06336v2 Announce Type: replace-cross Abstract: Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules.

By Yi Yang, Ovidiu Daescu

llms benchmarks

Related stories

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

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

Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks

KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction

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

BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning