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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Le-Yi Wei

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Sep 2026

SwinTransRNA: A Swin Transformer-Based Framework for Accurate Prediction of RNA Subcellular Localization

RNA subcellular localization determines the regulatory context in which microRNAs (miRNAs), circular RNAs (circRNAs), and long noncoding RNAs (lncRNAs) exert their functions, yet these RNA classes differ greatly in sequence length and circularity, making fixed-length raw-sequence representations inconvenient for cros...

Bo-Wen Shi, Xu-Xin He, Yen-Peng Chiu et al. · 0 citations
#diffusion models Open access Sep 2026

scLDM: a conditional diffusion framework for single-cell perturbation prediction.

MOTIVATION Accurate prediction of single-cell responses to external stimuli is pivotal for deciphering gene regulatory mechanisms and accelerating data-driven drug discovery. However, effectively capturing the complex, non-linear mapping between intrinsic cell states and external stimuli remains an open problem. RESU...

Bo-Yang Wu, Yu-Hang Liu, Yue Cheng et al. · 0 citations
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
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
Jul 2026

DDI-MMAF: Multi-modal affine fusion of visual and semantic representations for anticancer drug synergy prediction.

DDI-MMAF is proposed, a lightweight cross-modal framework that avoids using explicit high-dimensional omics profiles as direct model inputs and enables accurate synergy prediction from raw minimalist inputs, offering a practical and efficient computational solution for cost-effective drug combination discovery.

Hao Li, Qianhui Jiang, Jiahui Guan et al. · 0 citations

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