Summary of Pre-training with Fractional Denoising to Enhance Molecular Property Prediction, by Yuyan Ni et al.
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
by Yuyan Ni, Shikun Feng, Xin Hong, Yuancheng Sun, Wei-Ying Ma, Zhi-Ming Ma, Qiwei Ye, Yanyan Lan
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed molecular pre-training framework, fractional denoising (Frad), decouples noise design from the constraints imposed by force learning equivalence in deep learning methods for accelerating molecular screening. Frad introduces customizable noise, allowing for incorporating chemical priors to improve molecular distribution modeling. The framework consistently outperforms existing methods across force prediction, quantum chemical properties, and binding affinity tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Molecular scientists are working on a new way to understand molecules using computers. They’re trying to figure out how to train computers to learn about molecules without having labeled data. Some people have already tried this, but they didn’t consider the rules that govern molecules. This paper presents a new approach called fractional denoising (Frad) that lets them design noise in a way that makes sense for molecules. This helps the computer create better models of molecules and make more accurate predictions. |
Keywords
* Artificial intelligence * Deep learning
