Summary of Geoscatt-gnn: a Geometric Scattering Transform-based Graph Neural Network Model For Ames Mutagenicity Prediction, by Abdeljalil Zoubir and Badr Missaoui
GeoScatt-GNN: A Geometric Scattering Transform-Based Graph Neural Network Model for Ames Mutagenicity Prediction
by Abdeljalil Zoubir, Badr Missaoui
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Quantitative Methods (q-bio.QM)
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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 approach to predict mutagenicity by integrating three methods: using 2D scattering coefficients from molecular images, a hybrid model combining geometric graph scattering, Graph Isomorphism Networks, and machine learning, and a new graph neural network architecture called MOLG3-SAGE. These models demonstrate superior performance in predicting mutagenicity on the ZINC dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces three approaches to predict mutagenicity: using 2D molecular images, a hybrid model combining geometric graph scattering and machine learning, and a new graph neural network architecture. The results show that these methods can accurately predict whether a chemical compound is likely to cause mutations. |
Keywords
» Artificial intelligence » Graph neural network » Machine learning
