Summary of Could Chemical Llms Benefit From Message Passing, by Jiaqing Xie et al.
Could Chemical LLMs benefit from Message Passing
by Jiaqing Xie, Ziheng Chi
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed paper investigates bidirectional interactions between molecular structures and their textual representations using pre-trained language models (LMs) and message passing neural networks (MPNNs). The authors propose two strategies to evaluate whether integrating information from both models can enhance performance: contrast learning, which supervises the training of the LM with an MPNN, and fusion, which combines information from both models. Empirical analysis reveals that integration approaches outperform baselines on smaller molecular graphs, but not on large-scale ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can better understand molecules by combining two types of AI models: language models and message passing neural networks. Right now, these models are good at different things – the language model is great with text, while the message passing network is good with molecular structures. But what if we could combine the strengths of both? The authors try out a few ways to do this and find that it works better for smaller molecules than larger ones. |
Keywords
» Artificial intelligence » Language model
