Unveiling Molecular Moieties through Hierarchical Graph Explainability

Background: Graph Neural Networks (GNN) have emerged in very recent years as a powerful tool for supporting in silico Virtual Screening. In this work we present a GNN which uses Graph Convolutional architectures to achieve very accurate multi-target screening. We also devised a hierarchical Explainable Artificial Intelligence (XAI) technique to catch information directly at atom, ring, and whole molecule level by leveraging the message passing mechanism. In this way, we find the most relevant moieties involved in bioactivity prediction. Results: …

architectures artificial artificial intelligence cs.ai cs.lg explainability explainable artificial intelligence gnn graph graph neural networks hierarchical information intelligence networks neural networks q-bio.mn q-bio.qm screening through tool virtual work xai