Summary of Reacllama: Merging Chemical and Textual Information in Chemical Reactivity Ai Models, by Aline Hartgers et al.
ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models
by Aline Hartgers, Ramil Nugmanov, Kostiantyn Chernichenko, Joerg Kurt Wegner
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes novel approaches to improve the accuracy of chemical reactivity models. The current models are trained solely on chemical information, but neglect procedural text that can provide crucial details about synthetic protocols. Two methods are introduced: an adapter model that incorporates a latent representation of procedural text generated by GPT-2 (ReacLLaMA-Adapter), and a zero-shot labeling approach using LLaMA 2 to train the Graphormer model on an extended dataset. These methodologies enhance the prediction of unpromising reactions, leading to more accurate models with improved specificity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make chemical reactivity models better by adding information about how to do chemistry experiments. Right now, these models only look at what’s in the reaction, not how it’s done. Two new ways are suggested: one uses a special model (GPT-2) to help understand the steps involved and then trains another model (Graphormer) on that information. The other method uses a different big language model (LLaMA 2) to guess what should happen in certain reactions, and then trains Graphormer on those results. Both methods make it easier to predict when a reaction won’t work, making the models more accurate. |
Keywords
* Artificial intelligence * Gpt * Language model * Llama * Zero shot
