title: Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular
    Language Model
summary: Understanding molecules is key to understanding organisms and driving
  advances in drug discovery, requiring interdisciplinary knowledge across
  chemistry and biology. Although large molecular language models have achieved
  notable success in interpreting molecular structures, their instruction
  datasets are limited to the specific knowledge from task-oriented datasets and
  do not fully cover the fundamental characteristics of molecules, hindering
  their abilities as general-purpose molecular assistants. To address this issue,
  we propose Mol-LLaMA, a large molecular language model that grasps the general
  knowledge centered on molecules via multi-modal instruction tuning. To this
  end, we design key data types that encompass the fundamental features of
  molecules, incorporating essential knowledge from molecular structures. In
  addition, to improve understanding of molecular features, we introduce a module
  that integrates complementary information from different molecular encoders,
  leveraging the distinct advantages of different molecular representations. Our
  experimental results demonstrate that Mol-LLaMA is capable of comprehending the
  general features of molecules and generating relevant responses to users'
  queries with detailed explanations, implying its potential as a general-purpose
  assistant for molecular analysis.
paper_authors._id: 67b7ceae3e8a45f770b2606e
paper_authors.name: Dongki Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T09:59:11.214Z
paper_authors._id: 65633c5e84a9fbe322f87d81
paper_authors.avatarUrl: /avatars/7233a555b43c669847a950ce5697c92c.svg
paper_authors.fullname: DongkiKim
paper_authors.type: user
paper_authors.user: DongkiKim
paper_authors._id: 67b7ceae3e8a45f770b2606f
paper_authors.name: Wonbin Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T13:09:46.291Z
paper_authors._id: 66d812997e6c9509bb15fac2
paper_authors.avatarUrl: /avatars/baf0e384a864de47bfd989aebe62c357.svg
paper_authors.fullname: Wonbin Lee
paper_authors.type: user
paper_authors.user: WonbinLee067
paper_authors._id: 67b7ceae3e8a45f770b26070
paper_authors.name: Sung Ju Hwang
paper_title: Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular
    Language Model
paper_summary: Understanding molecules is key to understanding organisms and driving
  advances in drug discovery, requiring interdisciplinary knowledge across
  chemistry and biology. Although large molecular language models have achieved
  notable success in interpreting molecular structures, their instruction
  datasets are limited to the specific knowledge from task-oriented datasets and
  do not fully cover the fundamental characteristics of molecules, hindering
  their abilities as general-purpose molecular assistants. To address this issue,
  we propose Mol-LLaMA, a large molecular language model that grasps the general
  knowledge centered on molecules via multi-modal instruction tuning. To this
  end, we design key data types that encompass the fundamental features of
  molecules, incorporating essential knowledge from molecular structures. In
  addition, to improve understanding of molecular features, we introduce a module
  that integrates complementary information from different molecular encoders,
  leveraging the distinct advantages of different molecular representations. Our
  experimental results demonstrate that Mol-LLaMA is capable of comprehending the
  general features of molecules and generating relevant responses to users'
  queries with detailed explanations, implying its potential as a general-purpose
  assistant for molecular analysis.
paper_ai_keywords: large molecular language models multi-modal instruction tuning molecular encoders molecular representations general-purpose molecular assistant