Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular
  Language Model
Authors: name: Dongki Kim, name: Wonbin Lee, name: Sung Ju Hwang

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.