| title | Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints |
|---|---|
| authors | Thomas MacDougall, Maksim Kuznetsov, Roman Schutski, Rim Shayakhmetov, Maxim Malkov, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov |
| date | 2026-07-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.18144 |
| affiliation | Insilico Medicine |
| upvotes | 15 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{macdougall2026do,
title={Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints},
author={Thomas MacDougall and Maksim Kuznetsov and Roman Schutski and Rim Shayakhmetov and Maxim Malkov and Vladimir Aladinskiy and Alex Aliper and Alex Zhavoronkov},
year={2026},
eprint={2607.18144},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.18144}
}