Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular
  Language Model
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.
67b7ceae3e8a45f770b2606e
Dongki Kim
claimed_verified
2025-02-21T09:59:11.214Z
65633c5e84a9fbe322f87d81
/avatars/7233a555b43c669847a950ce5697c92c.svg
DongkiKim
user
DongkiKim
67b7ceae3e8a45f770b2606f
Wonbin Lee
admin_assigned
2025-02-24T13:09:46.291Z
66d812997e6c9509bb15fac2
/avatars/baf0e384a864de47bfd989aebe62c357.svg
Wonbin Lee
user
WonbinLee067
67b7ceae3e8a45f770b26070
Sung Ju Hwang
Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular
  Language Model
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.
Mol-LLaMA, a multi-modal instruction-tuned large molecular language model, integrates various molecular encoders to understand general features and provide detailed responses for molecular analysis tasks.
large molecular language model multi-modal instruction tuning molecular structures molecular encoders molecular representations