Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery
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.
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Jiatong Li
claimed_verified
2025-08-14T13:37:39.481Z
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Jiatong LI
user
phenixace
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Weida Wang
claimed_verified
2025-08-14T13:37:37.341Z
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Weida Wang
user
weidawang
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Qinggang Zhang
689d51fdb083e610d741e9f2
Junxian Li
claimed_verified
2025-08-14T13:37:33.168Z
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Junxian Li
user
Duke-de-Artois
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Di Zhang
claimed_verified
2025-08-14T13:37:35.471Z
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Di Zhang
user
di-zhang-fdu
689d51fdb083e610d741e9f4
Changmeng Zheng
689d51fdb083e610d741e9f5
Shufei Zhang
689d51fdb083e610d741e9f6
Xiaoyong Wei
689d51fdb083e610d741e9f7
Qing Li
Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery
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.
Mol-R1 framework enhances molecule discovery by improving reasoning performance and explainability through PRID and MoIA strategies.
Large language models Explicit Long Chain-of-Thought DeepSeek-R1 QWQ commonsense reasoning mathematical inference knowledge-intensive domains molecule discovery molecular structures chemical principles high-quality reasoning dataset Prior Regulation via In-context Distillation PRID MoIA Molecular Iterative Adaptation Supervised Fine-tuning SFT Reinforced Policy Optimization RPO text-based molecule generation