MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter

• URL: http://arxiv.org/abs/2310.12798v4
• Date: Thu, 18 Jan 2024 09:03:24 GMT
• Title: MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
• Authors: Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua
• Abstract summary: Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception. We propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter.
• Score: 91.77292826067465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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