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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Linlong Jiang

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#artificial intelligence Jun 2025

ChargeNet: E(3) Equivariant Graph Attention Network for Atomic Charge Prediction

This work introduces an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision, and improves the model's accuracy, generalization, and robustness in complex scenarios.

Qiaolin Gou, Qun Su, Ji-Ke Wang et al. · 1 citation
#machine learning Sep 2025

Revisiting Protein-Protein Docking: A Systematic Evaluation Framework

A unified benchmarking framework is established that enables systematic evaluation of docking methods across diverse tasks and provides critical insights into the strengths and limitations of current docking strategies, thereby informing future developments in protein-protein docking research.

Linlong Jiang, Ke Zhang, Kai Zhu et al. · 3 citations
#machine learning Jan 2026

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDoc...

Hui Zhang, Xujun Zhang, Qun Su et al. · 6 citations
#computer vision Open access Jul 2025

A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention

LiTEN achieves state-of-the-art accuracy on standard benchmarks, consistently outperforming leading approaches in both precision and speed, and enables comprehensive modeling tasks, ranging from geometry optimization to free energy surface construction, with high computational efficiency for large biomolecules.

Qun Su, Kai Zhu, Qiaolin Gou et al. · 2 citations
#machine learning Open access Jun 2026

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow

An integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002 is introduced.

Kai Zhu, Huating Wang, Jin-Tu Zhang et al. · 0 citations
#machine learning Open access Jun 2026

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios...

Yingjuan Cheng, Qing Ye, Linlong Jiang et al. · 0 citations

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