Chem-R: Learning to Reason as a Chemist

Abstract

Despite the potential of Large Language Models (LLMs) in chemical discovery, current LLMs still lack fundamental chemical domain knowledge, produce incoherent reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a general Chemical Reasoning model designed to emulate the deliberative processes of chemists. To build advanced reasoning capabilities of Chem-R, we design a three-phase training framework, including: 1) Chemical Foundation Training (CFT), which establishes core chemical knowledge. 2) Chemical Reasoning Protocol (CRP) Distillation, incorporating structured, expert-like reasoning traces to guide systematic and reliable problem solving. 3) Chemical Multi-Task Optimization (CMO) that optimizes the model for generalizable capabilities across diverse molecular- and reaction-level tasks. This structured pipeline enables Chem-R to achieve state-of-the-art performance on comprehensive benchmarks, surpassing leading LLMs, including Gemini-3-Pro and Kimi-k2.5, by up to 19% on molecular tasks and 40% on reaction tasks. Meanwhile, Chem-R also consistently outperforms existing chemical foundation models across both molecular and reaction level tasks. These results demonstrate Chem-R's superior generalization, interpretability, and potential as a foundation for next-generation AI-driven chemical discovery. The code and model are available at https://github.com/davidweidawang/Chem-R.