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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

Jianbo Qiao

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Open access Sep 2026

Reaction-aware hypergraph learning of chemical representations with LARK

Molecular pretraining offers a route to learning transferable chemical representations from unlabelled data. However, existing approaches pretrained on isolated molecular structures struggle to generalize to reaction-specific tasks because their pretraining objectives provide limited information about chemical transfor...

Jian-Bo Qiao, Yu-Hang Liu, Jun-Ru Jin et al. · 0 citations
Sep 2026

Adaptive prioritized expansion for cost-conscious retrosynthetic route planning.

Results indicate that incorporating molecular cost information into heuristic search can improve the practicality and economic efficiency of retrosynthetic planning.

Shuan Liu, Jing-Wen Wang, Shao-Ye Zhang et al. · 0 citations
Open access Sep 2026

A token-pruning framework enables efficient representation of the human genome for RNA modification analysis

Experimental results show that ATSFormer consistently outperforms existing state-of-the-art methods while achieving substantial computational savings and structural analysis using AlphaFold3 supports the biological relevance of the motifs identified by ATSFormer.

Wen-Jia Gao, Jun-Lei Yu, Jun-Ru Jin et al. · 0 citations
Jul 2026

Relation-Aware Pretraining and Reaction Center Modeling for Chemical Reaction Graph Representation Learning.

Evaluations across three downstream tasks show that KAGT achieves strong performance relative to existing baselines in reaction classification, reaction condition prediction, and yield prediction, supporting KAGT as a transferable representation framework for AI-driven chemical synthesis.

Jian-Bo Qiao, Ke-Fei Li, Jun-Ru Jin et al. · 1 citation
Aug 2026

Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.

Experimental results show that Hyp-Retro outperforms comparative methods in terms of search success rate and route yield, and it can adaptively adjust its planning strategy under different yield preferences, thereby generating high-quality retrosynthetic routes that better satisfy target-specific requirements.

Jing-Wen Wang, Shuan Liu, Shao-Ye Zhang et al. · 0 citations
Jul 2026

PeptideSGCL: Structure-Enhanced Graph-Transformer Encoding and Dual-Level Contrastive Learning for Peptide Property Prediction.

A multimodal dual-contrastive learning framework for peptide property prediction is proposed, which improves both the structural encoder and the contrastive learning strategy to enhance the quality of joint sequence-structure representations.

Jiajie Cai, Shuwen Xiong, Yuntao Yang et al. · 0 citations

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