BGC-QDR: A Quantum-Assisted Pipeline for Biosynthetic Gene Cluster Discovery and Ranking from Environmental DNA

Biosynthetic gene clusters (BGCs) encode enzymatic pathways for natural products with pharmaceutical potential, yet prioritizing candidates from fragmented environmental DNA (eDNA) assemblies remains computationally challenging. We present BGC-QDR (Biosynthetic Gene Cluster Quantum Discovery and Ranking), an open-source pipeline that integrates input quality control, Prodigal ORF prediction, Pfam HMM domain annotation, rule-based BGC classification, MiBIG 4.0 novelty assessment, and variational quantum classifier (VQC) ranking via PennyLane. BGC-QDR is designed as a quantum-assisted ranking framework for biologically informed BGC prioritization, not as a claim of quantum computational advantage over classical machine learning. We evaluate the pipeline on MiBIG 4.0 (2,636 annotated BGCs) using a 20-dimensional biosynthetic feature vector and stratified 10-fold cross-validation. The integrated VQC (6 qubits x 3 layers, 54 parameters) achieves accuracy of 0.789 {+/-} 0.076 and ROC-AUC of 0.835 {+/-} 0.057. Random Forest achieves the highest ROC-AUC (0.898 {+/-} 0.032), followed by Logistic Regression (0.874 {+/-} 0.020) and MLP (0.872 {+/-} 0.024). Wilcoxon signed-rank tests on per-fold AUC scores show that VQC ROC-AUC is significantly lower than Random Forest (p = 0.0098) and Logistic Regression (p = 0.037) at = 0.05, with no significant difference versus MLP (p = 0.064). Architecture ablation identifies 4 qubits x 3 layers as the best VQC configuration on hold-out validation (AUC = 0.737). Feature importance analysis highlights peptidyl carrier protein domains, cluster length, and module count as dominant predictors. BGC-QDR provides a reproducible, end-to-end workflow for eDNA-derived BGC discovery with integrated novelty scoring and quantum-assisted candidate ranking. The complete BGC-QDR source code, benchmark datasets, and reproduction instructions are publicly available at: Abhishekmishra2808/BGC-PIPELINE