用SMILES与分子图互译训练，让大模型真正学会“读懂”分子结构，而非死记符号。
arXiv:2607.03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular under…
新方法通过不确定性校准提升3D分子图生成的可靠性，有望推动药物发现与材料设计。
arXiv:2606.01595v1 Announce Type: new Abstract: Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networ…
零样本图学习新突破：自适应子图去噪结合大语言模型，告别一刀切，显著提升传统GNN泛化能力
arXiv:2603.02938v2 Announce Type: replace-cross Abstract: Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarci…
新框架SGR通过外部子图逐步引导LLM推理，提升复杂逻辑推理能力。
arXiv:2605.16117v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities across diverse NLP applications, su…
通过组合层次图的多粒度自监督预训练，MolCHG将键信息作为独立语义层，突破传统分子属性预测的单粒度局限。
arXiv:2605.16088v1 Announce Type: cross Abstract: Self-supervised pretraining on molecular graphs has emerged as a promising approach for molecular pr…