Graphs are how we represent most of the data and neural networks should be able to read them. This blog covers my work on building a Graph Neural Network for drug multitask classification using the PTC (Predictive Toxicology Challenge) dataset.

Introduction

The PTC dataset is a collection of chemical compounds with their toxicity labels across different species. The challenge is to predict whether a compound is toxic or not based on its molecular structure represented as a graph.

Graph Representation

In molecular graphs, atoms are represented as nodes and chemical bonds as edges. Each node has features like atomic number, charge, and hybridization state, while edges represent bond types (single, double, triple, aromatic).

Model Architecture

The GraphNet model uses message passing to aggregate information from neighboring nodes and edges, allowing the network to learn complex molecular patterns that determine toxicity.

Results

[Results and analysis would go here]

Conclusion

Graph Neural Networks show promise for drug discovery applications by effectively learning from molecular graph representations.