Authors
Goel, A., Mishra, P.
Abstract
Rapid and accurate pathogen detection remains a major challenge in modern bioinformatics, as existing tools are often fragmented and require multiple specialized workflows. We present AGPI (AI-powered Genomic Pathogen Intelligence), an integrated platform that combines genomic sequence classification, biological enrichment, three-dimensional structural visualization, and AI-guided therapeutic prioritization within a single interpretable pipeline. AGPI employs a hybrid convolutional Bidirectional Gated Recurrent Unit (BiGRU) architecture trained on DNA sequences from 40 pathogen classes spanning viruses, bacteria, fungi, and protozoan pathogens. The model achieved 99.61% validation accuracy and 94.90% accuracy on an independent held-out evaluation of 600 pathogen sequences following iterative refinement. As a proof of concept, AGPI correctly classified a Zika virus genome with 96.14% confidence, retrieved curated biological context from 245 peer-reviewed studies, and identified Ribavirin as a leading therapeutic candidate against the Zika NS5 polymerase through AI-guided molecular docking. Multi-metric ligand similarity analysis further differentiated candidate compounds according to their structural and pharmacological properties. These results demonstrate that integrated AI-driven genomic pipelines can accelerate pathogen characterization and therapeutic hypothesis generation while providing an accessible and interpretable framework for infectious disease surveillance and computational drug repurposing.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 12 Jul 2026.
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