Explainable AI for end-to-end pathogen target discovery and molecular design

Polonio, A.; Perez-Garcia, A.; Fernandez-Ortuno, D.; Jimenez-Castro, L.

doi:10.64898/2026.02.27.708593

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bioRxiv · 10.64898/2026.02.27.708593

Explainable AI for end-to-end pathogen target discovery and molecular design

Polonio, A.·Perez-Garcia, A.·Fernandez-Ortuno, D.·Jimenez-Castro, L.

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Abstract

Drug discovery is limited by target identification, a major bottleneck in antimicrobial development and in combating emerging fungicide resistance. We present APEX (Attention-based Protein EXplainer), an explainable AI framework for cross-species, proteome-scale target discovery and pocket-guided molecular design. APEX combines ESM-2 embeddings, GAT, and a MLP to train pathogen-specific essentiality predictors (APEX-Tar) alongside a universal druggability model (APEX-Drug). Attention maps and GNNExplainer-derived subgraphs highlight residues driving predictions, enabling direct conditioning of structure-based diffusion models for inhibitor generation. APEX-Tar identifies key residues in known fungal targets and nominates new candidates, including adenylosuccinate lyase (ADSL) in fungi and the bacterial adhesin YadV. APEX-Drug recapitulates established fungicide binding sites, guides the design of ADSL inhibitors exploiting a fungal-specific active-site residue, and uncovers in YadV a previously undescribed pocket distinct from known pilicide sites. Together, APEX provides a kingdom-agnostic, explainable pipeline for target prioritization and guided molecular design, accelerating the search for next-generation antimicrobials.

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Polonio, A., Perez-Garcia, A., Fernandez-Ortuno, D., Jimenez-Castro, L.. 2026-03-02. Explainable AI for end-to-end pathogen target discovery and molecular design. https://doi.org/10.64898/2026.02.27.708593

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