Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

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 (i.e., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector. Specifically, the cross-modal projector is implemented as a QFormer 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.

Discipline

Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, December 6-10

First Page

15623

Last Page

15638

Identifier

10.18653/v1/2023.emnlp-main.966

Publisher

Association for Computational Linguistics

City or Country

Singapore

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.18653/v1/2023.emnlp-main.966

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