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Xiaozhuang Song

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#machine learning Preprint Sep 2026

RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis

Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials design. Retrosynthesis involves molecular graph transformations that can change both connectivity and graph size. These transformations may introduce reactant components ab...

Xiao-Zhuang Song, Xue-Min Chen, Xinjian Zhao et al. · 0 citations
#machine learning Preprint Sep 2026

ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning

Large language models are increasingly expected to support diverse chemical reasoning capabilities within a unified model. One approach is to develop specialized capabilities separately and consolidate them through multi-teacher on-policy distillation, but this raises two questions: how should specialization be organiz...

Yao-Yao Xu, Xinjian Zhao, Xiao-Zhuang Song et al. · 0 citations
#natural language process... Preprint Aug 2026

Closed-Loop Bayesian Molecular Inverse Design with Semantic LLM Surrogates

Experiments show that BoMolLLM improves over one-shot prompting, is competitive with or stronger than GP-based BO baselines, and reveals a domain-dependent interface: reference-only transfer works best for binary drug targets, while adding a concise surrogate summary is more beneficial for continuous material.

Yao-Yao Xu, Xinjian Zhao, Xiao-Zhuang Song et al. · 0 citations
#machine learning Conference Open access Sep 2026

When Vision Meets Graphs: A Survey on Graph Reasoning and Learning

This survey provides a first systematic overview of the emerging area of vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning, and organize existing work into three threads.

Xinjian Zhao, Wei Pang, Zhixuan Yu et al. · 2 citations
Preprint Aug 2026

MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models

This work introduces \textbf{MolEmb}, a lightweight framework that adapts MLLMs by aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, and finds that context-aware molecular embedding is primarily a data property of the supervision.

Xinjian Zhao, Xiang-Ru Jian, Yao-Yao Xu et al. · 3 citations

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