MolBind: Multimodal Alignment of Language, Molecules, and Proteins
CoRR(2024)
摘要
Recent advancements in biology and chemistry have leveraged multi-modal
learning, integrating molecules and their natural language descriptions to
enhance drug discovery. However, current pre-training frameworks are limited to
two modalities, and designing a unified network to process different modalities
(e.g., natural language, 2D molecular graphs, 3D molecular conformations, and
3D proteins) remains challenging due to inherent gaps among them. In this work,
we propose MolBind, a framework that trains encoders for multiple modalities
through contrastive learning, mapping all modalities to a shared feature space
for multi-modal semantic alignment. To facilitate effective pre-training of
MolBind on multiple modalities, we also build and collect a high-quality
dataset with four modalities, MolBind-M4, including graph-language,
conformation-language, graph-conformation, and conformation-protein paired
data. MolBind shows superior zero-shot learning performance across a wide range
of tasks, demonstrating its strong capability of capturing the underlying
semantics of multiple modalities.
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