Summary of Hashing Based Contrastive Learning For Virtual Screening, by Jin Han et al.
Hashing based Contrastive Learning for Virtual Screening
by Jin Han, Yun Hong, Wu-Jun Li
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A novel deep learning-based approach for virtual screening in computer-aided drug discovery is proposed, which leverages contrastive learning to learn vector representations for both proteins and molecules. This method outperforms traditional docking methods but still incurs significant memory and time costs when dealing with large-scale molecular databases. To address this issue, the authors introduce a hashing-based contrastive learning method called DrugHash, which treats virtual screening as a retrieval task using efficient binary hash codes. Experimental results demonstrate that DrugHash achieves state-of-the-art accuracy while reducing memory usage by 32 times and processing speed by 3.5 times compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Virtual screening is an important step in finding new medicines. It’s like looking for the right key to unlock a door. Traditional ways of doing this take too long, so scientists have been trying to find faster ways using computers and math. They’ve had some success with deep learning, which is a type of artificial intelligence. Now, researchers propose a new method that uses something called hashing, which helps computers quickly find the right answers. This method, called DrugHash, is really good at finding molecules that bind to specific proteins. It’s faster than other methods and gives better results. |
Keywords
» Artificial intelligence » Deep learning
