Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments

Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven chemi nformatics, however the unique implications of different Graph Neural Network (GNN) architectures are poorly explored, for this space . This study compared performance s of Graph Convolutional Networks (GCN s), Graph Attention Networks (GAT s) and Graph Isomorphism Networks (GINs), applied to 7 different toxicological assay datasets of varying data abundance and endpoint, to perform binary classification of assay activation. Following pre - processing of molecular graphs, enforcement of class - balance and stratif ication of all datasets across 5 folds, Bayesian optimisations were carried out, for each GNN applied to each assay dataset (resulting in 21 unique Bayesian optimisations) . Optimised GNN s performed at Area Under the Curve (AUC) scores ranging from 0.728 - 0.849 (averaged across all folds), naturally varying between specific assays and GN Ns . GINs were found to consistently outperform GCNs and GAT s, for the top 5 of 7 most data - abundant toxicological assays . GAT s however significantly outperformed over the remaining 2 most data - scarce assays . This indicates that GINs are a more optimal architecture for data - abundant environments, whereas GAT s are a more optimal architecture for data - scarce environments . Subsequent analysis of the explored higher - dimensional hyperparameter spac es, as well as optimised hyperparameter states, found that GCNs and GAT s reached measurably closer optimised state s with each other, compared to GINs, further indicating the unique nature of GINs as a GNN algorithm .