arXiv AI June 19, 2026 By Mohamed Mouhajir, Limei Wang, El Houcine Bergou, Hajar El Hammouti, Lamiae Azizi, Dongqi Fu

Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs

arXiv:2606. 19374v1 Announce Type: cross Abstract: Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 15

Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

arXiv:2606. 14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery.

By Peng-Fei Sun, Chuan-Xian Ren, Hong Yan

benchmarks safety

arXiv Machine Learning

Aug 26

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

arXiv:2510.07289v2 Announce Type: replace Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...

By Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang

safety

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

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.

By Boshra Ariguib, Mathias Niepert, Andrei Manolache

llms rag diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 14

Inverse-IMPRESSION: A Graph-based Platform for Molecular Structure Elucidation from Experimental NMR Spectroscopic Properties

arXiv:2607. 09978v1 Announce Type: cross Abstract: Here, we present a platform built on our inverted Graph Transformer Network, IMPRESSION-G2, which can accurately and rapidly reconstruct molecular bonding directly from experimental nuclear magnetic resonance (NMR) spectroscopic information.

By Zheqi Jin, Grace Armitage, Richard Cox, Ben Honor\'e, Mohammad Golbabaee, Craig Butts

llms

arXiv AI

Aug 5

GraphCliff: Short-Long Range Gating for Modeling Critical Activity Changes Caused by Subtle Molecular Differences

arXiv:2511. 03170v3 Announce Type: replace-cross Abstract: The quantitative structure-activity relationship assumes a smooth mapping between molecular structure and biological activity.

By Hajung Kim, Jueon Park, Junseok Choe, Seungheun Baek, Hyeon Hwang, Jaewoo Kang

rag benchmarks

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Related stories

Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

Inverse-IMPRESSION: A Graph-based Platform for Molecular Structure Elucidation from Experimental NMR Spectroscopic Properties

GraphCliff: Short-Long Range Gating for Modeling Critical Activity Changes Caused by Subtle Molecular Differences