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A Deep Hypergraph Learning Model for Predicting Antimicrobial Combination Effects Across Bacterial Targets

Antimicrobial resistance (AMR) creates an urgent need for efficient strategies to identify effective antibacterial combinations. Combination therapy, including antimicrobial peptides (AMPs) paired with conventional antibiotics, is a promising approach, but exhaustive experimental screening across drug pairs and bacterial targets is impractical. This study introduces a hybrid GCN-based hypergraph neural network (HGNN) for predicting antimicrobial-agent combination outcomes against bacterial targets. Each antimicrobial-agent-antimicrobial-agent-bacterium triplet is represented as a ternary hyperedge, enabling the model to learn context-dependent interaction patterns. The framework integrates SMILES-derived molecular graph embeddings for antimicrobial agents, including conventional antibiotics and AMPs, with taxonomy-derived bacterial representations. The prediction task was formulated as a three-class classification problem: synergy, antagonism, and non-interaction. The non-interaction class included experimentally verified indifferent records and synthetic presumed non-interaction triplets generated by negative sampling. Model development used drug-pair-grouped splitting, five-fold grouped cross-validation within the training/validation partition, and final evaluation on a held-out test set. On the held-out three-class test set, the selected GCN-based HGNN achieved an accuracy of 0.83, weighted F1-score of 0.84, macro F1-score of 0.80, and ROC-AUC of 0.95. Per-class evaluation showed accuracies of 0.80 for synergy, 0.92 for antagonism, and 0.85 for non-interaction. Pair-type analysis showed strong performance across AMP-AMP, AMP-conventional antibiotic, and conventional antibiotic-conventional antibiotic combinations. These findings suggest that hypergraph-based representation learning can support computational prioritization of antimicrobial combinations for experimental follow-up. Further studies will be needed to improve model interpretability and to perform prospective validation of predicted synergistic combinations.

Software compensation of trigger-synchronous control-frame errors in qubits and qudits

arXiv:2606.00358v2 Announce Type: replace Abstract: Quantum control experiments are often subject to coherent, time-dependent disturbances that vary over timescales comparable to the experiment duration. We show that when such disturbances are reproducible with respect to a trigger signal, their effect can be measured and compensated through software-defined updates to the control frequency and phase. We experimentally verify the performance of our protocol using a trapped $^{137}$Ba$^+$ ion experiencing magnetic-field-induced energy shifts synchronous with the laboratory AC mains power. Using this compensation technique, the calibrated AC line contribution to the instantaneous oscillator detuning is reduced by $21(9)\times$, and the fitted AC-induced phase amplitude is reduced below the measurement uncertainty. We use randomized benchmarking to validate the compensation performance in quantum gate sequences, recovering an average single-qubit gate fidelity of 99.93(1)\% with a magnetic-field-sensitive qubit. Finally, we extend the compensation framework to multi-level qudit control. Using the Bernstein-Vazirani algorithm as a benchmark, we increase the algorithm's success probability from 10(7)\% to 70(9)\% in a 16 level system when compensation is enabled. Our results demonstrate that trigger-synchronized coherent errors can be reframed as deterministic control-frame errors and corrected in software.