Molecular Geometry Understanding Has Unintendedly Emerged in Frontier Large Language Models

Abstract

Large language models (LLMs) have already shown strong capabilities in solving complex chemical problems expressed in natural language. Yet, many tasks performed by chemists require understanding of 3D structures of chemical compounds and reasoning about them -- abilities, which, to the best of our knowledge, were neither intended by LLM developers nor tested in contemporary models. Such understanding is essential for autonomous molecular discovery and drug development, which is one of the Holy Grails of AI application to chemistry. We tested this capability in modern LLMs on 810 geometries and DFT energies of 27 small organic molecules. Strikingly, while models released before July 2025 struggled to rank conformers by stability, many recent frontier models, including GPT-5.6 Sol, Kimi K3, and Gemini 3.6 Flash achieved competitive accuracy, outperforming the Universal Force Field, and GPT-6 Astra and Claude Opus 5 came very close to a modern GFN-FF force field. Analysis of models'explanations for their rankings suggests that they perform best for molecules with well-defined intramolecular interactions -- hydrogen bonds -- which are well quantified in scientific language; at the same time all models except the two newest -- GPT-6 Astra and Claude Opus 5 -- struggle with molecules governed by loosely defined concepts (e.g., ring strain), where force fields excel, suggesting that the scientific language itself might impose constraints on LLMs. Notably, a model's ability to rank conformers is strongly associated with its performance on scientific, coding, and abstract-reasoning benchmarks, suggesting that it emerged unintendedly from models training on linked but conceptually different tasks. Thanks to this generalization of mere scientific knowledge into understanding molecular structures, current frontier models offer a realistic starting point for AI-driven molecular design.