Genetic algorithms (GA) are computational methods for solving optimization problems inspired by natural selection. Because we can simulate the quantum circuits that implement GA in different highly configurable noise models and even run GA on actual quantum computers, we can analyze this class of heuristic methods in the quantum context for NP-hard problems. This paper proposes an instantiation of the Reduced Quantum Genetic Algorithm (RQGA) that solves the NP-hard graph coloring problem in O(N^1/2). The proposed implementation solves both vertex and edge coloring and can also determine the chromatic number (i.e., the minimum number of colors required to color the graph). We examine the results, analyze the algorithm convergence, and measure the algorithm's performance using the Qiskit simulation environment. Our Reduced Quantum Genetic Algorithm (RQGA) circuit implementation and the graph coloring results show that quantum heuristics can tackle complex computational problems more efficiently than their conventional counterparts.

Background: Widespread bioinformatics applications such as drug repositioning or drug–drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets—we know the drug–drug or drug–target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications. Results: We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug–drug and drug–target interaction networks—built with data from DrugBank versions released over the plast decade—to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug–drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug–target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves. Conclusions: Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug–target interaction prediction and drug–drug interaction severity standardization.

Drug–drug interactions (DDIs) can either enhance or diminish the positive or negative effects of the associated drugs. Multiple drug combinations create difficulties in identifying clinically relevant drug interactions; this is why electronic drug interaction checkers frequently report DDI results inconsistently. Our paper aims to analyze drug interactions in cardiovascular diseases by selecting drugs from pharmacotherapeutic subcategories of interest according to Level 2 of the Anatomical Therapeutic Chemical (ATC) classification system. We checked DDIs between 9316 pairs of cardiovascular drugs and 25,893 pairs of cardiovascular and other drugs. We then evaluated the overall agreement on DDI severity results between two electronic drug interaction checkers. Thus, we obtained a fair agreement for the DDIs between drugs in the cardiovascular category, as well as for the DDIs between drugs in the cardiovascular and other (i.e., non-cardiovascular) categories, as reflected by the Fleiss’ kappa coefficients of $\kappa =0.3363$ and $\kappa =0.3572$, respectively. The categorical analysis of agreement between ATC-defined subcategories reveals Fleiss’ kappa coefficients that indicate levels of agreement varying from poor agreement ($\kappa <0$) to perfect agreement ($\kappa =1$). The main drawback of the overall agreement assessment is that it includes DDIs between drugs in the same subcategory, a situation of therapeutic duplication seldom encountered in clinical practice. Our main conclusion is that the categorical analysis of the agreement on DDI is more insightful than the overall approach, as it allows a more thorough investigation of the disparities between DDI databases and better exposes the factors that influence the different responses of electronic drug interaction checkers. Using categorical analysis avoids potential inaccuracies caused by particularizing the results of an overall statistical analysis in a heterogeneous dataset.

Background: Drug-drug interactions (DDIs) are a growing safety concern in dental care, particularly among patients with comorbidities and polypharmacy. However, real-world data (RWD) on the prevalence and severity of DDIs in dental settings remain scarce. Objectives: This study aimed to assess the frequency, severity, and clinical relevance of DDIs in dental patients and to identify age- and comorbidity-related risk patterns. Methods: This retrospective study analyzed a cohort of 105 dental patients, considering demographics, preexisting diseases, dental procedures, and prescribed medications. We examined drug-drug interactions (DDIs) employing the DrugBank Drug Interaction Checker, which yields DDI severity into major, moderate, or minor. Results: 45.7% of patients had preexisting diseases, with cardiovascular diseases most prevalent (19.0%). Higher prevalent dental diagnoses and procedures included apical lesions (47.6%) and tooth extractions (53.3%), suggesting frequent pharmacotherapy exposure. We identified 542 DDIs out of 1332 drug pairs and found 2.3% major, 25.0% moderate, and 13.4% minor, with 59.3% showing no interactions. Key high-risk DDIs included epinephrine with beta-blockers. Fifteen patients aged 31–60 years experienced the most major DDIs of 61.3%, patients ≥ 61 years faced 38.7%, and the 0–30 group had none, highlighting age-specific risks. The higher DDIs burden in the 31–60 age group may reflect better knowledge of the drugs they used and accurate reporting of them. Conclusions: Our retrospective study addresses the paucity of dental DDIs real-world data (RWD) studies, pleading for improved drug reconciliation, systematic screening, and age- and comorbidity-tailored strategies to enhance patient safety.

We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ${\epsilon }^2$: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (${\epsilon }^2$ ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin $\Delta$ = +0.13 in Community 2; vinblastine $\Delta$ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology.