Quantum multiple kernel learning (Q²SAR) will outperform classical QSAR in predicting antimicrobial peptide (AMP) potency against WHO GLASS-surveilled resistant pathogens by ≥25% AUC-ROC when trained on homology-controlled AMPBench-MT data, due to its ability to capture non-linear interactions between peptide secondary structure and resistance gene expression patterns.
Quantum multiple kernel learning (Q²SAR) will outperform classical QSAR in predicting antimicrobial peptide (AMP) potency against WHO GLASS-surveilled resistant pathogens by ≥25% AUC-ROC when trained on homology-controlled AMPBench-MT data, due to its ability to capture non-linear interactions between peptide secondary structure and resistance gene expression patterns.
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Supporting Research Papers
- \mathtt{Q^2SAR}: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning
Quantitative Structure-Activity Relationship (\mathtt{QSAR}) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, b...
- AMPBench-MT: A Homology-Controlled Benchmark for Antimicrobial Peptide Potency, Spectrum, and Safety Prediction
Computational AMP discovery is often evaluated through AMP/non-AMP recognition, yet follow-up decisions depend on assay-derived evidence such as target-species potency, hemolysis, toxicity, and select...
- Quantum Sampling Architecture for Protein Structure Reconstruction on Utility-Scale Hardware
Predicting the structure of short peptides in protein binding pockets remains difficult because this regime requires physics-based conformational search, yet existing methods do not provide a practica...
- Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support
Background: Antimicrobial resistance (AMR) is a global health threat. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) provides standardized data, population-level mac...
- Forecasting Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support
Antimicrobial resistance (AMR) is a growing global crisis projected to cause 10 million deaths per year by 2050. While the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) provi...
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