Fragment-Based Explainable AI for Pathogen-Selective Antimicrobial Drug Design

Here, we present a fragment-based explainable artificial intelligence (XAI) framework, based on Relational Graph Convolutional Network (R-GCN) models trained with over 127,000 compounds from the Community for Open Antimicrobial Drug Discovery (CO-ADD) database and ChEMBL, targeting <i>Staphylococcus aureus</i> (Gram-positive), <i>Escherichia coli</i> (Gram-negative), and <i>Candida albicans</i> (fungal).

External validation on 100,000 high-throughput screening compounds from the European Chemical Biology Database (ECBD) demonstrated the predictive performance of the three models and their utility in selecting compounds from a structurally divergent library, with enrichment factors up to 19-fold. Using substructure mask explanation (SME), we further decompose each prediction into fragment-based contribution scores and map them onto the chemical structures, allowing medicinal chemists to identify which scaffolds and substituents have a positive or negative effect on the activity against specific pathogens.