  Language Models (LMs) have demonstrated impressive molecule understanding
ability on various 1D text-related tasks. However, they inherently lack 2D
graph perception - a critical ability of human professionals in comprehending
molecules' topological structures. To bridge this gap, we propose MolCA:
Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal
Adapter. MolCA enables an LM (e.g., Galactica) to understand both text- and
graph-based molecular contents via the cross-modal projector. Specifically, the
cross-modal projector is implemented as a Q-Former to connect a graph encoder's
representation space and an LM's text space. Further, MolCA employs a uni-modal
adapter (i.e., LoRA) for the LM's efficient adaptation to downstream tasks.
Unlike previous studies that couple an LM with a graph encoder via cross-modal
contrastive learning, MolCA retains the LM's ability of open-ended text
generation and augments it with 2D graph information. To showcase its
effectiveness, we extensively benchmark MolCA on tasks of molecule captioning,
IUPAC name prediction, and molecule-text retrieval, on which MolCA
significantly outperforms the baselines. Our codes and checkpoints can be found
at https://github.com/acharkq/MolCA.
