Summary of Molbind: Multimodal Alignment Of Language, Molecules, and Proteins, by Teng Xiao et al.
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
by Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. Honavar
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary MolBind is a pre-training framework that tackles the challenge of processing multiple molecular modalities (natural language, 2D/3D molecular graphs, and 3D proteins) by mapping all modalities to a shared feature space for multi-modal semantic alignment. The proposed framework trains encoders for each modality through contrastive learning. To facilitate effective pre-training, a high-quality dataset called MolBind-M4 is built and collected, featuring four paired modalities (graph-language, conformation-language, graph-conformation, and conformation-protein). Experimental results demonstrate superior zero-shot learning performance across various tasks, showcasing MolBind’s ability to capture the underlying semantics of multiple modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MolBind is a new way to help computers understand different types of information about molecules. Right now, we can only train computers on two types of information at once. This makes it hard for them to learn from all the different kinds of data that scientists have collected. MolBind changes this by letting computers learn from many different types of information at once. To make this work, researchers built a huge dataset with lots of examples of molecules and how they can be described in different ways (like words or pictures). This helps the computer learn to understand what all these different descriptions are talking about, even if it’s never seen that specific molecule before. |
Keywords
* Artificial intelligence * Alignment * Multi modal * Semantics * Zero shot
