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20232026
most citedRethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules

5 citations · 15 across the 19 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.LG2025

Reasoning-Enhanced Large Language Models for Molecular Property Prediction

Jiaxi Zhuang, Yaorui Shi, Jue Hou +8

Molecular property prediction is crucial for drug discovery and materials science, yet existing approaches suffer from limited interpretability, poor cross-task generalization, and…

cs.CL2025

Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents

Yaorui Shi, Yuxin Chen, Siyuan Wang +5

Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens. Existing works equip large langu…

cs.CL2025

Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning

Yaorui Shi, Sihang Li, Chang Wu +5

Large language models have demonstrated impressive reasoning capabilities but are inherently limited by their knowledge reservoir. Retrieval-augmented reasoning mitigates this limi…

cs.LG2025

Language-Enhanced Representation Learning for Single-Cell Transcriptomics

Yaorui Shi, Jiaqi Yang, Changhao Nai +5

Single-cell RNA sequencing (scRNA-seq) offers detailed insights into cellular heterogeneity. Recent advancements leverage single-cell large language models (scLLMs) for effective r…

q-bio.QM2025★ 2 cited

NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation

Zhiyuan Liu, Yanchen Luo, Han Huang +7

3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers,…

cs.LG2025★ 3 cited

Intelligent System for Automated Molecular Patent Infringement Assessment

Yaorui Shi, Sihang Li, Taiyan Zhang +12

Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven pro…