Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
Score
79首次系统评估LLM在三维空间分子生成中的能力，填补了该领域的空白。
实验设计全面，对比基线合理，但论文尚未公开代码，可重复性待验证。
为AI药物研发中模型选型提供直接指导，3D-Fit基准可被社区复用。
论文发布于2025年7月，正值LLM与药物设计交叉领域快速发展的阶段。
来自arXiv预印本，作者为活跃在计算生物学和AI交叉领域的团队。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
Innovative 3D-Fit benchmark fills the gap in evaluating LLM spatial reasoning; rigorous experimental design with impactful conclusions.
AI commentary
「This paper is worth attention because it rigorously compares LLMs against diffusion models in 3D molecular generation for the first time. If you are debating between diffusion models and LLMs for drug design, the data and benchmark here provide critical decision support. For AI researchers, it reveals LLM weaknesses and potential in spatial reasoning, inspiring new architectures.」
Quick overview
This paper systematically analyzes the ability of general-purpose large language models (LLMs) to handle 3D spatial constraints in structure-based drug design (SBDD). Compared to specialized models like diffusion models, LLMs still lag behind in multi-conditioned spatial molecule generation tasks such as protein pocket constraints, pharmacophore points, and mandatory interactions, but show promise in handling multiple spatial constraints simultaneously. The authors introduce 3D-Fit, a token-efficient benchmarking strategy, to evaluate LLMs and reveal their scalability to heterogeneous setups.
- LLMs lag behind diffusion models in multi-conditioned spatial molecule generation but show potential to handle multiple constraints simultaneously.
- Introduce 3D-Fit benchmark for efficient evaluation of LLMs under complex spatial constraints.
- Findings reveal LLM spatial reasoning capabilities are scalable to heterogeneous setups, laying groundwork for unified models.
- Systematic comparison between general-purpose LLMs and specialized diffusion models in drug design.
Full text
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