Summary of Understanding Active Learning Of Molecular Docking and Its Applications, by Jeonghyeon Kim et al.
Understanding active learning of molecular docking and its applications
by Jeonghyeon Kim, Juno Nam, Seongok Ryu
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)
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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 investigates the effectiveness of active learning methodologies in predicting docking scores using only 2D structures, without considering three-dimensional structural features. The authors empirically validate the efficacy of these methodologies through benchmark studies encompassing six receptor targets. They find that surrogate models tend to memorize structural patterns prevalent in high docking-scored compounds obtained during acquisition steps. Despite this tendency, surrogate models demonstrate utility in virtual screening, as exemplified in the identification of actives from DUD-E dataset and high docking-scored compounds from EnamineReal library. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special way of searching for new medicines by looking at lots of possible combinations of molecules. It’s like trying to find a needle in a haystack, but instead of using needles and hay, it uses computer simulations. The researchers want to see if they can use computers to guess which molecules are likely to work well without having to look at all the details of how they’re shaped. They found that the computers were pretty good at this, especially when they were looking for molecules that worked really well. This could help scientists find new medicines more quickly and efficiently. |
Keywords
* Artificial intelligence * Active learning
