Arboviruses such as dengue, Zika, chikungunya and yellow fever share similar clinical manifestations, making differential diagnosis challenging, particularly in endemic regions with viral co‐circulation. Conventional laboratory methods present important limitations, including cross‐reactivity and reliance on specialized infrastructure. In this scenario, spectroscopic techniques such as Fourier‐transform attenuated total reflectance infrared spectroscopy (ATR–FTIR) and Raman, when combined with artificial intelligence (AI), have shown promise by enabling rapid, low‐cost analyses. This systematic review (PROSPERO CRD420251006929) aimed to qualitatively and quantitatively synthesize studies that applied infrared and Raman spectroscopy to clinical samples, supported by chemometric models. All 23 included studies investigated dengue patients, with only one also assessing Zika and chikungunya. Most studies employed Raman spectroscopy and multivariate analysis methods, such as principal component analysis with linear discriminant analysis (PCA‐LDA, 39.1%) and partial least squares with discriminant analysis (PLS‐DA, 21.7%), with an overall sensitivity of 0.94 (95% CI: 0.91–0.96) and overall specificity of 0.97 (95% CI: 0.95–0.98) for Raman spectroscopy. The risk of bias across all studies was high, according to PROBAST‐AI development and evaluation assessment. These findings highlight the potential of spectroscopic approaches combined with AI for diagnosing arboviral infections, although further robust studies are required to support broader clinical validation.

Neuropsychiatric disorders often involve dysregulation of serotonergic and dopaminergic pathways. This study applied machine learning (ML) with quantitative structure-activity relationship (QSAR) methods to predict the bioactivity (-log half-maximal inhibitory concentration (pIC50)) of compounds targeting both receptor families, aiming to identify multitarget inhibitors among US Food and Drug Administration (FDA)-approved drugs. A dataset of 5,628 compounds with experimental IC50 values was obtained from ChEMBL and encoded with PubChem fingerprints. Random Forest and Extreme Gradient Boosting models were trained, optimized, and evaluated with 5-fold cross-validation, and Shapley Additive Explanations (SHAP) values were used for interpretation. After outlier removal and descriptor selection, models achieved coefficient of determination (R2) test ca. 0.69 and were used to screen over 1,500 approved drugs. A total of 162 were predicted to have dual bioactivity (pIC50 > 6, coefficient of variation < 1%), including antipsychotics, adrenergic agonists (e.g., epinephrine), dopamine agonists (e.g., levodopa), antihistamines (e.g., cyproheptadine), antiemetics (e.g., droperidol), ergot alkaloids (e.g., ergotamine), antibiotics (e.g., penicillin G), and lipid-lowering agents (e.g., pravastatin). Key molecular descriptors indicated the relevance of nitrogen-containing fragments and conjugated aromatic substructures for dual receptor binding. These results provide a computational framework for repurposing drugs and guiding experimental validation in neuropsychiatric research.