# Nature Language Model: Deciphering the Language of Nature for Scientific Discovery

arXiv: https://arxiv.org/abs/2502.07527 · HTML: https://mlsanity.com/paper/2502.07527/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 30 of 36)

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- [LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning](https://mlsanity.com/paper/2602.07075/) (2026)
- [The Need for Verification in AI-Driven Scientific Discovery](https://mlsanity.com/paper/2509.01398/) (2025)
- [Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis](https://mlsanity.com/paper/2601.17687/) (2026)
- [AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials](https://mlsanity.com/paper/2510.04704/) (2025)
- [When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs](https://mlsanity.com/paper/2602.03554/) (2026)
- [Improving Chemical Understanding of LLMs via SMILES Parsing](https://mlsanity.com/paper/2505.16340/) (2025)
- [MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models](https://mlsanity.com/paper/2512.21231/) (2025)
- [Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences](https://mlsanity.com/paper/2606.16905/) (2026)
- [Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs](https://mlsanity.com/paper/2510.01527/) (2025)
- [MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design](https://mlsanity.com/paper/2510.27671/) (2025)
- [SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning](https://mlsanity.com/paper/2509.21861/) (2025)
- [AMix-2: Establishing Protein as a Native Modality in Large Language Models](https://mlsanity.com/paper/2605.30963/) (2026)
- [BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language](https://mlsanity.com/paper/2606.22138/) (2026)
- [Automated Data Readiness for Scientific AI](https://mlsanity.com/paper/2607.02771/) (2026)
- [Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling](https://mlsanity.com/paper/2510.22123/) (2025)
- [Lost in Tokenization: Context as the Key to Unlocking Biomolecular Understanding in Scientific LLMs](https://mlsanity.com/paper/2510.23127/) (2025)
- [A Scientific Reasoning Model for Organic Synthesis Procedure Generation](https://mlsanity.com/paper/2512.13668/) (2025)
- [Multi-scale Graph Autoregressive Modeling: Molecular Property Prediction via Next Token Prediction](https://mlsanity.com/paper/2601.02530/) (2026)
- [MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery](https://mlsanity.com/paper/2603.03517/) (2026)
- [Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models](https://mlsanity.com/paper/2603.03946/) (2026)
- [Deciphering Scientific Reasoning Steps from Outcome Data for Molecule Optimization](https://mlsanity.com/paper/2603.20262/) (2026)
- [RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design](https://mlsanity.com/paper/2604.17175/) (2026)
- [SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models](https://mlsanity.com/paper/2605.19357/) (2026)

## Abstract (arXiv)

Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspired by the success of these foundation models, researchers have developed foundation models for individual scientific domains, including small molecules, materials, proteins, DNA, RNA and even cells. However, these models are typically trained in isolation, lacking the ability to integrate across different scientific domains. Recognizing that entities within these domains can all be represented as sequences, which together form the "language of nature", we introduce Nature Language Model (NatureLM), a sequence-based science foundation model designed for scientific discovery. Pre-trained with data from multiple scientific domains, NatureLM offers a unified, versatile model that enables various applications including: (i) generating and optimizing small molecules, proteins, RNA, and materials using text instructions; (ii) cross-domain generation/design, such as protein-to-molecule and protein-to-RNA generation; and (iii) top performance across different domains, matching or surpassing state-of-the-art specialist models. NatureLM offers a promising generalist approach for various scientific tasks, including drug discovery (hit generation/optimization, ADMET optimization, synthesis), novel material design, and the development of therapeutic proteins or nucleotides. We have developed NatureLM models in different sizes (1 billion, 8 billion, and 46.7 billion parameters) and observed a clear improvement in performance as the model size increases.