---
title: "语言模型能梦见结合分子吗？——在空间约束下基准测试LLM"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.18144v1"
permalink: "https://feed.astralor.com/items/itm_675453eb1836302121639365"
published: "2026-07-20T16:43:54.000Z"
score: 79
channel: "ai"
tags: ["3D-Fit","LLM","基于结构的药物设计","扩散模型","空间推理"]
---

# 语言模型能梦见结合分子吗？——在空间约束下基准测试LLM

## AI 点评

> 这篇论文值得关注，因为它首次把LLM放在三维空间分子生成的聚光灯下做严格比对。如果你在药物研发中纠结于选扩散模型还是尝试LLM路线，这里的数据和基准就是你需要的决策依据。对AI研究者来说，它也揭示了LLM在空间推理上的短板和潜力，可能启发新的架构设计。

## 速览

本文系统分析通用大语言模型（LLM）在基于结构的药物设计（SBDD）中处理三维空间约束的能力。与扩散模型等专用模型相比，LLM在蛋白质口袋约束、药效团点、必需相互作用等多条件空间分子生成任务上仍落后，但已展现出同时处理多种空间约束的潜力。作者提出3D-Fit基准测试策略，评估LLM在多条件空间分子生成中的表现，揭示其可扩展至异构设置的前景。

## 关键点

- LLM在多条件空间分子生成中落后于扩散模型，但具备同时处理多个约束的潜力。
- 提出3D-Fit基准测试，高效评估LLM在复杂空间约束下的生成能力。
- 研究发现LLM的空间推理能力可扩展至异构设置，为未来统一模型奠定基础。
- 系统比较了通用LLM与专用扩散模型在药物设计中的性能差异。

## 全文

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.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_675453eb1836302121639365