Explainable AI for end-to-end pathogen target discovery and molecular design
Polonio, A.; Perez-Garcia, A.; Fernandez-Ortuno, D.; Jimenez-Castro, L.
Show abstract
Drug discovery is often constrained by target identification, a bottleneck especially acute in antimicrobial development and the fight against 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 evolutionary embeddings, graph attention networks, and a multilayer perceptron to train pathogen-specific essentiality and virulence predictors (APEX-Tar) alonsgside a universal druggability model (APEX-Drug). Attention maps and GNNExplainer-derived subgraphs highlight residues and pockets driving predictions, enabling direct conditioning of structure-based diffusion models for inhibitor generation. APEX-Tar recovers known fungal targets (endopolygalacturonase 1, Hog1 MAPK) and proposes new candidates, including fungal GmrSD and bacterial YadV. APEX-Drug recapitulates established fungicide sites ({beta}-tubulin, cytochrome b), guides putative inhibitor design for GmrSD, and identifies in YadV a previously undescribed pocket distinct from known pilicide sites. Together, APEX offers a kingdom-agnostic pipeline for explainable target prioritization and guided molecular design.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP) 94%
- Interpreting tree ensemble machine learning models with endoR 93%
- Knowledge-guided data mining on the standardized architecture of NRPS: subtypes, novel motifs, and sequence entanglements 93%
Similar papers in this journal
- Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins 95%
- ProT-Diff: A Modularized and Efficient Approach to De Novo Generation of Antimicrobial Peptide Sequences through Integration of Protein Language Model and Diffusion Model 95%
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model 94%
Similar papers in this journal
- Direct prediction of intrinsically disordered protein conformational properties from sequence 95%
- Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions 95%
- Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.