Summary of Bridging the Gap Between Learning and Inference For Diffusion-based Molecule Generation, by Peidong Liu et al.
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
by Peidong Liu, Wenbo Zhang, Xue Zhe, Jiancheng Lv, Xianggen Liu
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a training framework called GapDiff to address exposure bias issues in molecular generation using diffusion models. The framework utilizes model-predicted conformations as ground truth probabilistically during training, aiming to mitigate the data distributional disparity between training and inference. Experimental results on the CrossDocked2020 dataset show that GapDiff enhances the affinity of generated molecules with superior vina energy and diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to create new molecules that can help us understand how things work at a molecular level. It uses special models called diffusion models to generate these molecules, but it’s hard to make them look like real molecules. The researchers came up with a way to fix this problem by using the model’s predictions as if they were true, which makes the generated molecules more realistic and useful. |
Keywords
» Artificial intelligence » Diffusion » Inference
