用强化学习与可验证奖励机制提升分子生成质量，AI驱动科学发现新路径
arXiv:2607.19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in che…
利用潜在扩散模型平滑分子暗区，提升分子生成质量与多样性，创新性论文工具。
arXiv:2606.13955v1 Announce Type: new Abstract: Latent diffusion is a promising framework for scalable 3D molecular generation, but it requires a late…
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…
小语言模型学会分子语法，高效生成药物分子候选，AI+化学新突破。
arXiv:2605.06322v2 Announce Type: replace Abstract: Language models for molecular design have scaled to hundreds of millions of parameters, yet how th…
LLM在开放域自然语言驱动分子生成中的表现评估，新基准揭示文本到分子对齐的挑战。
arXiv:2412.14642v4 Announce Type: replace Abstract: Recently, Large Language Models (LLMs) have demonstrated great potential in natural language-drive…
商空间扩散模型：用商空间思想巧妙处理系统对称性，实现更高效、保对称性的分子3D结构生成。
arXiv:2604.21809v2 Announce Type: replace-cross Abstract: Diffusion-based generative models have reformed generative AI, and also enabled new capabili…
提出首个统一框架，用强化学习后训练攻克分子图生成的可控性难题，解决原子级动作空间和化学无效中间态问题
arXiv:2605.15354v1 Announce Type: new Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lack…