这篇论文系统测了GPT等LLM在3D分子生成上的表现，发现它们虽不如专用扩散模型，但能同时处理多种约束，值得关注。
结构药物设计(SBDD)利用蛋白质靶标3D结构生成候选分子，扩散模型是主流方法。本工作引入3D-Fit基准测试，评估LLM在多约束空间分子生成中的表现，包括蛋白口袋、锚定片段、药效团点及口袋-配体相互作用。结果显示，LLMs虽落后于专用扩散模型，但能同时处理多种空间约束，展现出可扩展的潜力。
Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules. While diffusion models have dominated as a leading paradigm for high-quality 3D molecule generation, LLM-based methods are rapidly emerging in molecular design and have shown competitive performance in pocket-conditioned molecular generation. However, their ability to reason about physics and 3D spatial environments is largely underexplored. In this work, we systematically analyze whether current general-purpose LLMs are capable of navigating complex 3D constraints compared to established baselines such as specialized diffusion models. We consider 3D ligand generation conditioned on protein pockets together with ligand- and interaction-derived spatial constraints, including anchor fragments, pharmacophore points, and mandatory pocket-ligand interactions. To enable this evaluation, we introduce 3D-Fit - a token-efficient benchmarking strategy for assessing LLM performance on multi-conditioned spatial molecule generation. Our findings reveal a clear pattern in LLM spatial capabilities: while they still lag behind state-of-the-art approaches, they are promising and can handle multiple spatial constraints simultaneously, enabling scaling to heterogeneous setups.