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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

Yu Kang

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#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 Nov 2025

mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset

The authors develop mRNABERT, a foundational AI model that designs entire mRNA sequences and demonstrates superior performance across comprehensive benchmarks, which signifies a substantial leap forward in mRNA research and therapeutic development.

Ying Xiong, Aowen Wang, Yu Kang et al. · 23 citations · ⚡1
#machine learning Open access May 2025

Token-Mol 1.0: tokenized drug design with large language models

Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream application...

Ji-Ke Wang, Rui Qin, Mingyang Wang et al. · 30 citations · ⚡1
#machine learning Open access Jun 2025

HiCLR: Knowledge-Induced Hierarchical Contrastive Learning with Retrosynthesis Prediction Yields a Reaction Foundation Model

HiCLR is the first foundation model that can be broadly applied to various synthesis-related tasks, and it achieves state-of-the-art performance in reaction classification, reaction condition recommendation, reaction yield prediction, synthesis planning, and even molecular property prediction.

Jialu Wu, Yiheng Zhu, Xiaorui Wang et al. · 0 citations
#machine learning Open access Aug 2026

AI-driven PROTAC design overcomes oncogenic resilience by eliminating the CLIP1-LTK fusion protein.

The AI-guided development of a first-in-class proteolysis-targeting chimera (PROTAC) designed to selectively degrade the CLIP1-LTK fusion protein is reported, providing a promising therapeutic strategy for overcoming acquired resistance in kinase-driven cancers.

Shi-Cheng Chen, Hai-Ting Duan, S. Zhong et al. · 0 citations
#machine learning Open access Apr 2026

Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning

ERAM aligns pre-trained molecular representations from Protein Language Model with the knowledge of enzyme catalysis by modeling enzymatic reactions as multi-relational data, and demonstrates its potential as a versatile and effective tool for enzyme catalysis research.

Yuansheng Huang, Lanqing Li, Wenjia Qian et al. · 2 citations
#natural language process... Open access Apr 2026

LaMGen: LLM-based 3D molecular generation for multi-target drug design

This study introduces LaMGen, an LLM-powered framework that leverages large-scale protein-ligand data and rotation-aware molecular encoding to rapidly produce chemically plausible multi-target candidates, achieving strong zero-shot generalization, superior molecular quality, and robust performance across dual- and trip...

Qun Su, Qiaolin Gou, Hui Zhang et al. · 1 citation
#machine learning Open access Jul 2026

BBBP-Atlas: Unified Interpretable Modeling of Blood–Brain Barrier Permeability across Small Molecules and Peptides

BBBP-Atlas is proposed, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP, which offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug d...

Xin Shen, Qun Su, Hao Luo et al. · 0 citations
#machine learning Jul 2026

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms

ProphDR is an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism, and generates biologically interpretable attention maps that highlight key pharmacophores and resistance-related genes consistent with established mechanisms in N...

Yundian Zeng, Qing Ye, Jike Wang 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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