Summary of Motive: a Drug-target Interaction Graph For Inductive Link Prediction, by John Arevalo et al.
MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction
by John Arevalo, Ellen Su, Anne E Carpenter, Shantanu Singh
First submitted to arxiv on: 12 Jun 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 introduces MOTIVE, a dataset for predicting drug-target interactions using Cell Painting features. The dataset comprises 11,000 genes and 3,600 compounds with their relationships extracted from seven publicly available databases. The authors provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Benchmark results show that graph neural networks using Cell Painting features outperform those relying on graph structure alone, feature-based models, and topological heuristics. This work accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MOTIVE is a new tool for predicting how drugs interact with genes. It’s like a big dictionary that helps scientists understand which medicines might work best on certain parts of our bodies. The team created this dataset by combining information from many different sources. They also came up with special ways to test the predictions, making sure they’re accurate and reliable. Using MOTIVE can help scientists develop new medicines faster and more effectively. |
