# Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery

arXiv: https://arxiv.org/abs/2508.08401 · HTML: https://mlsanity.com/paper/2508.08401/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [OpenAI o1 System Card](https://mlsanity.com/paper/2412.16720/) (2024)
- [A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language](https://mlsanity.com/paper/2209.05481/) (2022)
- [An Empirical Study on Eliciting and Improving R1-like Reasoning Models](https://mlsanity.com/paper/2503.04548/) (2025)
- [Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation](https://mlsanity.com/paper/2412.14642/) (2024)
- [MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts](https://mlsanity.com/paper/2411.14721/) (2024)

## Built on by (most cited first, 12 of 12)

- [Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis](https://mlsanity.com/paper/2601.17687/) (2026)
- [Retro-Expert: Collaborative Reasoning for Interpretable Retrosynthesis](https://mlsanity.com/paper/2508.10967/) (2025)
- [BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search](https://mlsanity.com/paper/2601.11037/) (2026)
- [Chemical Chain-of-Thought Functions as a Hallucination-Prone Molecular Scratchpad](https://mlsanity.com/paper/2607.20935/) (2026)
- [DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model](https://mlsanity.com/paper/2508.18579/) (2025)
- [ChemVLR: Prioritizing Reasoning in Perception for Chemical Vision-Language Understanding](https://mlsanity.com/paper/2604.06685/) (2026)
- [ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design](https://mlsanity.com/paper/2604.16896/) (2026)
- [PolyReal: A Benchmark for Real-World Polymer Science Workflows](https://mlsanity.com/paper/2604.02934/) (2026)
- [Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design](https://mlsanity.com/paper/2604.20254/) (2026)
- [MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models](https://mlsanity.com/paper/2603.18256/) (2026)
- [MolGVR: A Chemistry-Grounded Framework for Text-to-Molecule Generation](https://mlsanity.com/paper/2607.29479/) (2026)
- [MolLean: A Process-Verifiable Language for Chemical Reasoning](https://mlsanity.com/paper/2610.20459/) (2026)

## Abstract (arXiv)

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.