Summary of Graphprint: Extracting Features From 3d Protein Structure For Drug Target Affinity Prediction, by Amritpal Singh
GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction
by Amritpal Singh
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 GraphPrint framework integrates three-dimensional (3D) protein structure features for accurate drug target affinity prediction, improving the drug discovery process and reducing production costs. By combining graph representations of protein structures with drug graphs and traditional features, the model learns to predict binding affinity. On the KIBA dataset, GraphPrint achieves a mean square error of 0.1378 and a concordance index of 0.8929, outperforming traditional feature-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphPrint is a new way to predict how well drugs bind to proteins, which helps find better drug candidates faster. Scientists used to only look at the protein’s building blocks (amino acids), but now they also consider its 3D shape, which affects how it interacts with drugs. By combining this new information with other important details, the GraphPrint model makes more accurate predictions. |
