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Explainable AI for end-to-end pathogen target discovery and molecular design
1 APEX introduces a unified explainable AI framework combining ESM-2 protein embeddings, graph attention networks, and diffusion models to bridge target identification and inhibitor design in a single pipeline.
2 The dual-model architecture features APEX-Tar for pathogen-specific essentiality/virulence prediction and APEX-Drug as a universal druggability classifier, enabling cross-species proteome-scale target discovery.
3 Built-in interpretability through attention weights and GNNExplainer provides residue-level mechanistic insights, directly guiding structure-based diffusion models to generate pocket-specific inhibitors without black-box limitations.
4 Applied to Botrytis cinerea, APEX recovered known fungal targets including endopolygalacturonase 1 and Hog1 MAPK, while identifying 805 high-confidence novel candidates enriched in necrotrophic virulence functions.
5 The pipeline successfully designed candidate inhibitors for GmrSD, a top-ranked fungal target, with predicted binding energies of -7.6 to -7.3 kcal/mol and favorable drug-like properties.
6 Cross-kingdom generalization was demonstrated by retraining APEX-Tar on bacterial virulence factors and applying it to Acinetobacter baumannii, where YadV fimbrial chaperone emerged as the top target with a previously undescribed druggable pocket.
7 The YadV case revealed both the canonical pilicide-binding groove and a novel pocket distinct from known sites, with generated inhibitors showing strong predicted affinities (-8.9 and -8.8 kcal/mol) and distinct interaction patterns.
8 This work addresses the critical bottleneck in antimicrobial discovery by integrating interpretable target prioritization with rational molecular generation, offering a scalable blueprint for addressing resistance challenges in agriculture and medicine.
💻Code: github.com/Brunxi/APEX
📜Paper: biorxiv.org/content/10.648…
#ExplainableAI #DrugDiscovery #AntimicrobialResistance #GraphNeuralNetworks #ProteinLanguageModels #MolecularDesign #Bioinformatics #ComputationalBiology
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