title: NatureLM: Deciphering the Language of Nature for Scientific Discovery
summary: 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, and RNA. 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 (briefly, 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) achieving
  state-of-the-art performance in tasks like SMILES-to-IUPAC translation and
  retrosynthesis on USPTO-50k. 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.
paper_authors._id: 67ac1eaac61306b0ac95d2c6
paper_authors.name: Yingce Xia
paper_authors._id: 67ac1eaac61306b0ac95d2c7
paper_authors.name: Peiran Jin
paper_authors._id: 67ac1eaac61306b0ac95d2c8
paper_authors.name: Shufang Xie
paper_authors._id: 67ac1eaac61306b0ac95d2c9
paper_authors.name: Liang He
paper_authors._id: 67ac1eaac61306b0ac95d2ca
paper_authors.name: Chuan Cao
paper_authors._id: 67ac1eaac61306b0ac95d2cb
paper_authors.name: Renqian Luo
paper_authors._id: 67ac1eaac61306b0ac95d2cc
paper_authors.name: Guoqing Liu
paper_authors._id: 67ac1eaac61306b0ac95d2cd
paper_authors.name: Yue Wang
paper_authors._id: 67ac1eaac61306b0ac95d2ce
paper_authors.name: Zequn Liu
paper_authors._id: 67ac1eaac61306b0ac95d2cf
paper_authors.name: Yuan-Jyue Chen
paper_authors._id: 67ac1eaac61306b0ac95d2d0
paper_authors.name: Zekun Guo
paper_authors._id: 67ac1eaac61306b0ac95d2d1
paper_authors.name: Yeqi Bai
paper_authors._id: 67ac1eaac61306b0ac95d2d2
paper_authors.name: Pan Deng
paper_authors._id: 67ac1eaac61306b0ac95d2d3
paper_authors.name: Yaosen Min
paper_authors._id: 67ac1eaac61306b0ac95d2d4
paper_authors.name: Ziheng Lu
paper_authors._id: 67ac1eaac61306b0ac95d2d5
paper_authors.name: Hongxia Hao
paper_authors._id: 67ac1eaac61306b0ac95d2d6
paper_authors.name: Han Yang
paper_authors._id: 67ac1eaac61306b0ac95d2d7
paper_authors.name: Jielan Li
paper_authors._id: 67ac1eaac61306b0ac95d2d8
paper_authors.name: Chang Liu
paper_authors._id: 67ac1eaac61306b0ac95d2d9
paper_authors.name: Jia Zhang
paper_authors._id: 67ac1eaac61306b0ac95d2da
paper_authors.name: Jianwei Zhu
paper_authors._id: 67ac1eaac61306b0ac95d2db
paper_authors.name: Kehan Wu
paper_authors._id: 67ac1eaac61306b0ac95d2dc
paper_authors.name: Wei Zhang
paper_authors._id: 67ac1eaac61306b0ac95d2dd
paper_authors.name: Kaiyuan Gao
paper_authors._id: 67ac1eaac61306b0ac95d2de
paper_authors.name: Qizhi Pei
paper_authors._id: 67ac1eaac61306b0ac95d2df
paper_authors.name: Qian Wang
paper_authors._id: 67ac1eaac61306b0ac95d2e0
paper_authors.name: Xixian Liu
paper_authors._id: 67ac1eaac61306b0ac95d2e1
paper_authors.name: Yanting Li
paper_authors._id: 67ac1eaac61306b0ac95d2e2
paper_authors.name: Houtian Zhu
paper_authors._id: 67ac1eaac61306b0ac95d2e3
paper_authors.name: Yeqing Lu
paper_authors._id: 67ac1eaac61306b0ac95d2e4
paper_authors.name: Mingqian Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:34:24.363Z
paper_authors._id: 662f15453f9804ca5f9c1fd3
paper_authors.avatarUrl: /avatars/f7d514a7c7ef08f16dbde35310d0d8b6.svg
paper_authors.fullname: Mingqian Ma
paper_authors.type: user
paper_authors.user: Mishamq
paper_authors._id: 67ac1eaac61306b0ac95d2e5
paper_authors.name: Zun Wang
paper_authors._id: 67ac1eaac61306b0ac95d2e6
paper_authors.name: Tian Xie
paper_authors._id: 67ac1eaac61306b0ac95d2e7
paper_authors.name: Krzysztof Maziarz
paper_authors._id: 67ac1eaac61306b0ac95d2e8
paper_authors.name: Marwin Segler
paper_authors._id: 67ac1eaac61306b0ac95d2e9
paper_authors.name: Zhao Yang
paper_authors._id: 67ac1eaac61306b0ac95d2ea
paper_authors.name: Zilong Chen
paper_authors._id: 67ac1eaac61306b0ac95d2eb
paper_authors.name: Yu Shi
paper_authors._id: 67ac1eaac61306b0ac95d2ec
paper_authors.name: Shuxin Zheng
paper_authors._id: 67ac1eaac61306b0ac95d2ed
paper_authors.name: Lijun Wu
paper_authors._id: 67ac1eaac61306b0ac95d2ee
paper_authors.name: Chen Hu
paper_authors._id: 67ac1eaac61306b0ac95d2ef
paper_authors.name: Peggy Dai
paper_authors._id: 67ac1eaac61306b0ac95d2f0
paper_authors.name: Tie-Yan Liu
paper_authors._id: 67ac1eaac61306b0ac95d2f1
paper_authors.name: Haiguang Liu
paper_authors._id: 67ac1eaac61306b0ac95d2f2
paper_authors.name: Tao Qin
paper_title: NatureLM: Deciphering the Language of Nature for Scientific Discovery
paper_summary: 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, and RNA. 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 (briefly, 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) achieving
  state-of-the-art performance in tasks like SMILES-to-IUPAC translation and
  retrosynthesis on USPTO-50k. 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.
paper_ai_summary: Nature Language Model (NatureLM) integrates multiple scientific domains through sequence-based training, enabling diverse applications in discovery and optimization.
paper_ai_keywords: nature language model sequence-based science foundation model small molecules proteins RNA materials cross-domain generation SMILES-to-IUPAC translation retrosynthesis ADMET optimization drug discovery novel material design therapeutic proteins nucleotides