What does this research mean for the field?

A fragment-based explainable artificial intelligence framework using Relational Graph Convolutional Networks can accurately predict pathogen-selective antimicrobial activity and identify specific chemical scaffolds responsible for this activity to guide rational drug design. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop an explainable artificial intelligence framework that predicts pathogen-selective antimicrobial activity while providing interpretable chemical fragment insights for rational drug design.

September 14, 2026ACS Infectious DiseasesOpen Access

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

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Authors

AOAbdulmujeeb T. OnawoleThe University of Queensland MBMark A. T. BlaskovichThe University of Queensland JZJohannes ZueggThe University of Queensland

Discussion

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Implication

Computational study demonstrates fragment-level antimicrobial activity prediction across diverse pathogens, highlighting actionable pathways for rational antibiotic design.

Key Points

To develop an explainable artificial intelligence framework that predicts pathogen-selective antimicrobial activity while providing interpretable chemical fragment insights for rational drug design.
Trained Relational Graph Convolutional Network (R-GCN) models on over 127,000 compounds from the CO-ADD and ChEMBL databases targeting Staphylococcus aureus, Escherichia coli, and Candida albicans.
Validated predictive performance externally on 100,000 high-throughput screening compounds from the European Chemical Biology Database (ECBD).
Applied substructure mask explanation (SME) to derive fragment-level contribution scores and extract pathogen-selective core scaffolds.
The predictive models yielded enrichment factors of up to 19-fold when identifying active hits from a structurally divergent external compound library.
Fragment-based contribution scoring successfully mapped positive and negative substituent effects onto chemical structures, isolating core scaffolds selective for specific bacterial and fungal species.

Cite This Study

Onawole et al. (2026) studied this question.

synapsesocial.com/papers/6aa7dfc60926e14a848b3d53 https://doi.org/10.1021/acsinfecdis.6c00325

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September 14, 2026ACS Infectious DiseasesOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

AOAbdulmujeeb T. OnawoleThe University of Queensland MBMark A. T. BlaskovichThe University of Queensland JZJohannes ZueggThe University of Queensland

Discussion

Member takes

Implication

Computational study demonstrates fragment-level antimicrobial activity prediction across diverse pathogens, highlighting actionable pathways for rational antibiotic design.

Key Points

To develop an explainable artificial intelligence framework that predicts pathogen-selective antimicrobial activity while providing interpretable chemical fragment insights for rational drug design.
Trained Relational Graph Convolutional Network (R-GCN) models on over 127,000 compounds from the CO-ADD and ChEMBL databases targeting Staphylococcus aureus, Escherichia coli, and Candida albicans.
Validated predictive performance externally on 100,000 high-throughput screening compounds from the European Chemical Biology Database (ECBD).
Applied substructure mask explanation (SME) to derive fragment-level contribution scores and extract pathogen-selective core scaffolds.
The predictive models yielded enrichment factors of up to 19-fold when identifying active hits from a structurally divergent external compound library.
Fragment-based contribution scoring successfully mapped positive and negative substituent effects onto chemical structures, isolating core scaffolds selective for specific bacterial and fungal species.

Cite This Study

Onawole et al. (2026) studied this question.

synapsesocial.com/papers/6aa7dfc60926e14a848b3d53 https://doi.org/10.1021/acsinfecdis.6c00325

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