Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery
Authors: name: Jiatong Li, name: Weida Wang, name: Qinggang Zhang, name: Junxian Li, name: Di Zhang, name: Changmeng Zheng, name: Shufei Zhang, name: Xiaoyong Wei, name: Qing Li

Large language models (LLMs), especially Explicit Long Chain-of-Thought (CoT)
reasoning models like DeepSeek-R1 and QWQ, have demonstrated powerful reasoning
capabilities, achieving impressive performance in commonsense reasoning and
mathematical inference. Despite their effectiveness, Long-CoT reasoning models
are often criticized for their limited ability and low efficiency in
knowledge-intensive domains such as molecule discovery. Success in this field
requires a precise understanding of domain knowledge, including molecular
structures and chemical principles, which is challenging due to the inherent
complexity of molecular data and the scarcity of high-quality expert
annotations. To bridge this gap, we introduce Mol-R1, a novel framework
designed to improve explainability and reasoning performance of R1-like
Explicit Long-CoT reasoning LLMs in text-based molecule generation. Our
approach begins with a high-quality reasoning dataset curated through Prior
Regulation via In-context Distillation (PRID), a dedicated distillation
strategy to effectively generate paired reasoning traces guided by prior
regulations. Building upon this, we introduce MoIA, Molecular Iterative
Adaptation, a sophisticated training strategy that iteratively combines
Supervised Fine-tuning (SFT) with Reinforced Policy Optimization (RPO),
tailored to boost the reasoning performance of R1-like reasoning models for
molecule discovery. Finally, we examine the performance of Mol-R1 in the
text-based molecule reasoning generation task, showing superior performance
against existing baselines.