  Recently, Large Language Models (LLMs) with their strong task-handling
capabilities have shown remarkable advancements across a spectrum of fields,
moving beyond natural language understanding. However, their proficiency within
the chemistry domain remains restricted, especially in solving professional
molecule-related tasks. This challenge is attributed to their inherent
limitations in comprehending molecules using only common textual
representations, i.e., SMILES strings. In this study, we seek to enhance the
ability of LLMs to comprehend molecules by designing and equipping them with a
multi-modal external module, namely MolX. In particular, instead of directly
using a SMILES string to represent a molecule, we utilize specific encoders to
extract fine-grained features from both SMILES string and 2D molecular graph
representations for feeding into an LLM. Moreover, a human-defined molecular
fingerprint is incorporated to leverage its embedded domain knowledge. Then, to
establish an alignment between MolX and the LLM's textual input space, the
whole model in which the LLM is frozen, is pre-trained with a versatile
strategy including a diverse set of tasks. Extensive experimental evaluations
demonstrate that our proposed method only introduces a small number of
trainable parameters while outperforming baselines on various downstream
molecule-related tasks ranging from molecule-to-text translation to
retrosynthesis, with and without fine-tuning the LLM.
