arXiv AI June 3, 2026 By Zaifei Yang, Samuel Ping-Man Choi, James Kwok

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.

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

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 12

HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

arXiv:2512. 15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality.

By Yi Zhou, Haohao Qu, Yunqing Liu, Shanru Lin, Le Song, Wenqi Fan

llms diffusion fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jun 29

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

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.

By Giosue Migliorini, Aristofanis Rontogiannis, Grigori Guitchounts, Nicholas Franklin, Axel Elaldi, Olivia Viessmann

llms rag diffusion safety

arXiv AI

Jul 3

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.

By Wenbo Zhang

rag multimodal benchmarks

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 Machine Learning

Jun 10

Flexible Kernels for Protein Property Prediction

arXiv:2606. 11057v1 Announce Type: new Abstract: Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a significant challenge.

By Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry N. Ward, Hunter Nisonoff, James M. McFarland, Gevorg Grigoryan

rag

Related stories

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

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

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

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

Flexible Kernels for Protein Property Prediction