English(EN) SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks?

大型语言模型代理在新的SMDD-Bench药物设计任务中表现不佳

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员推出了SMDD-Bench，这是一个旨在评估大型语言模型代理在小分子药物设计能力的新基准。该基准包含502个任务实例，涵盖五个类别，包括骨架跳跃和先导优化，涉及102个独特的蛋白质靶点。即使是表现最好的模型GPT-5.4，也只能解决其中40.2%的复杂任务，这凸显了在实现完全自主的计算药物设计方面仍然存在重大挑战。 AI

影响凸显了大型语言模型代理在复杂科学领域中的当前局限性，为自主药物设计的未来研究指明方向。

排序理由该集群描述了一篇介绍用于评估大型语言模型在特定科学领域能力的基准的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Kevin Han, Renfei Zhang, Kathy Wei, Hamed Mahdavi, Niloofar Mireshghallah, Amir Barati Farimani · 2026-05-26 04:00

SMDD-Bench：LLM能否解决现实世界中的小分子药物设计任务？

arXiv:2605.21740v2 Announce Type: replace Abstract: LLM agents have incredible potential for scientific discovery applications. However, the performance of LLM agents on real-world, small molecule drug design (SMDD) tasks across diverse chemistries and targets is unclear. Current…

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报道来源 [1]

SMDD-Bench：LLM能否解决现实世界中的小分子药物设计任务？

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