Author

Jun-Ru Jin

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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. · bioRxiv · 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. · Bioinformatics · 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. · Journal of Chemical Theory a... · 1 citation