arXiv AI

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.

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

arXiv AI

Jul 28

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning

arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.

By Chen Wang, Boming Kang, Qinghua Cui

llms fine-tuning benchmarks

arXiv AI

Jun 2

Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction

arXiv:2606. 01781v1 Announce Type: new Abstract: Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic target discovery.

By Enqiang Zhu, Yizi Liu, Yilong Luo, Yao Chen, Yu Zhang, Baoshan Ma

diffusion benchmarks

arXiv Machine Learning

Jul 28

Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

arXiv:2607. 23518v1 Announce Type: new Abstract: The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination.

By Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang, Yunhong Lu, Chenxi Cai, Yan Zhang, Min Zhang

benchmarks safety

arXiv Machine Learning

Jun 16

Learning Topological Representations for Molecular Dynamics

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt

rag

arXiv AI

Aug 12

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.

By Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao, Heng-Jui Chang, Zeqi Zhou, Haokai Zhao, Ma Jian, Carl Ma, Yu-Chi Cheng, Kuan Pang, Xiangru Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng-Ann Heng, Naoto Yokoya, Masashi Sugiyama, Li Erran Li, Jure Leskovec, Yejin Choi

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Aug 5

DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing

arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.

By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen

rag multimodal benchmarks

Related stories