Summary of Presto: Progressive Pretraining Enhances Synthetic Chemistry Outcomes, by He Cao and Yanjun Shao and Zhiyuan Liu and Zijing Liu and Xiangru Tang and Yuan Yao and Yu Li
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes
by He Cao, Yanjun Shao, Zhiyuan Liu, Zijing Liu, Xiangru Tang, Yuan Yao, Yu Li
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Chemical Physics (physics.chem-ph)
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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 abstract discusses the application of Multimodal Large Language Models (MLLMs) in synthetic chemistry, a field that aims to design and conduct chemical reactions to synthesize new compounds with desired properties. Current approaches neglect the critical role of multiple molecule graph interactions, leading to suboptimal performance. The study introduces PRESTO, a framework that integrates pretraining strategies and dataset configurations to bridge the molecule-text modality gap. It uses cross-modal alignment and multi-graph understanding to progressively improve MLLMs. The framework achieves competitive results in downstream synthetic chemistry tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper explores how to use artificial intelligence (AI) models to help chemists create new compounds with specific properties. Currently, AI models don’t take into account the complex interactions between molecules, which leads to limited success. This study introduces a new approach called PRESTO that combines different techniques and datasets to better understand chemical reactions. The results show that this approach can be effective in helping chemists design new compounds. |
Keywords
* Artificial intelligence * Alignment * Pretraining
