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MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
Guojiang Zhao, Zixiang Lu, Yutang Ge +13
Large Language Models (LLMs) have shown impressive performance across various domains, but their ability to perform molecular reasoning remains underexplored. Existing methods most…
On the Design of One-step Diffusion via Shortcutting Flow Paths
Haitao Lin, Peiyan Hu, Minsi Ren +5
Recent advances in few-step diffusion models have demonstrated their efficiency and effectiveness by shortcutting the probabilistic paths of diffusion models, especially in trainin…
From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale
Yongqi Jin, Yecheng Wang, Jun-jie Wang +3
Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet existing machine learning metho…
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…
Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling
Shuqi Lu, Haowei Lin, Lin Yao +6
3D structure modeling is essential across scales, enabling applications from fluid simulation and 3D reconstruction to protein folding and molecular docking. Yet, despite shared 3D…
SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction
Haitao Lin, Junjie Wang, Zhifeng Gao +5
The essence of a chemical reaction lies in the redistribution and reorganization of electrons, which is often manifested through electron transfer or the migration of electron pair…