Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints

2026-07-20

AI Summary

This study investigates whether general-purpose large language models (LLMs) can effectively handle complex three-dimensional spatial constraints—such as protein binding pockets, pharmacophores, and anchor fragments—in structure-based drug design to generate plausible binding molecules. To this end, the authors introduce 3D-Fit, the first evaluation framework tailored for multi-spatial-constraint scenarios, which integrates a token-efficient benchmarking strategy with a constraint modeling approach conditioned on heterogeneous spatial information. Experimental results demonstrate that, although current LLMs slightly underperform state-of-the-art diffusion models in generation quality, they already exhibit a promising capacity to jointly incorporate diverse 3D constraints and show strong potential for scalability, thereby validating their prospective utility in structure-based drug design.

Abstract

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.

Problem

• language models
• 3D spatial constraints
• molecular generation
• structure-based drug design
• spatial reasoning

Innovation

• 3D-Fit
• spatial constraints
• LLM-based molecular design
• structure-based drug design
• multi-conditioned molecule generation