Unified Molecular Modeling via Modality Blending

• URL: http://arxiv.org/abs/2307.06235v1
• Date: Wed, 12 Jul 2023 15:27:06 GMT
• Title: Unified Molecular Modeling via Modality Blending
• Authors: Qiying Yu, Yudi Zhang, Yuyan Ni, Shikun Feng, Yanyan Lan, Hao Zhou, Jingjing Liu
• Abstract summary: We introduce a novel "blend-then-predict" self-supervised learning method (MoleBLEND) MoleBLEND blends atom relations from different modalities into one unified relation for matrix encoding, then recovers modality-specific information for both 2D and 3D structures. Experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D benchmarks.
• Score: 35.16755562674055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised molecular representation learning is critical for molecule-based tasks such as AI-assisted drug discovery. Recent studies consider leveraging both 2D and 3D information for representation learning, with straightforward alignment strategies that treat each modality separately. In this work, we introduce a novel "blend-then-predict" self-supervised learning method (MoleBLEND), which blends atom relations from different modalities into one unified relation matrix for encoding, then recovers modality-specific information for both 2D and 3D structures. By treating atom relationships as anchors, seemingly dissimilar 2D and 3D manifolds are aligned and integrated at fine-grained relation-level organically. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (inter-modal prediction) and mask-then-predict (intra-modal prediction) objectives into a single cohesive blend-then-predict framework.

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