Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a multifactorial condition requiring multi-target therapeutic strategies beyond traditional single-marker approaches. In this work, we present a fully in silico nutraceutical screening pipeline that integrates molecular prediction, systemic aggregation, and technological design. A curated panel of ten MASLD-relevant targets, spanning nuclear receptors (FXR, PPAR-α/γ, THR-β), lipogenic and cholesterogenic enzymes (ACC1, FASN, DGAT2, HMGCR), and transport/regulatory proteins (LIPG, FABP4), was assembled from proteomic evidence. Bioactivity records were extracted from ChEMBL, structurally standardized, and converted into RDKit descriptors. Predictive modeling employed a stacked ensemble of Random Forest, XGBoost, and CatBoost with isotonic calibration, yielding robust performance (mean cross-validated ROC-AUC 0.834; independent test ROC-AUC 0.840). Calibrated probabilities were aggregated into total activity (TA) and weighted TA metrics, combined with structural clustering (six structural clusters, twelve MOA clusters) to ensure chemical diversity. We used physiologically based pharmacokinetic (PBPK) modeling to translate probabilistic profiles into minimum simulated doses (MSDs) and chrono-specific exposure (%T>IC50) for three prototype concepts: HepatoBlend (morning powder), LiverGuard Tea (evening aqueous form), and HDL-Chews (postprandial chew). Integration of physicochemical descriptors (MW, logP, TPSA) guided carrier and encapsulation choices, addressing stability and sensory constraints. The results demonstrate that a computationally integrated pipeline can rationally generate multi-target nutraceutical formulations, linking molecular predictions with systemic coverage and practical formulation specifications, and thus provides a transferable framework for MASLD and related metabolic conditions.

The interaction of estrogen receptor alpha (ERα) with various metabolites—both endogenous and exogenous, such as those present in food products, as well as gut microbiota-derived metabolites—plays a critical role in modulating the hormonal balance in the human body. In this study, we evaluated a suite of 27 machine learning models and, following systematic optimization and rigorous performance comparison, identified linear discriminant analysis (LDA) as the most effective predictive approach. A meticulously curated dataset comprising 75 molecular descriptors derived from compounds with known ERα activity was assembled, enabling the model to achieve an accuracy of 89.4% and an F1 score of 0.93, thereby demonstrating high predictive efficacy. Feature importance analysis revealed that both topological and physicochemical descriptors—most notably FractionCSP3 and AromaticProportion—play pivotal roles in the potential binding to ERα. Subsequently, the model was applied to chemicals commonly encountered in food products, such as indole and various phenolic compounds, indicating that approximately 70% of these substances exhibit activity toward ERα. Moreover, our findings suggest that food processing conditions, including fermentation, thermal treatment, and storage parameters, can significantly influence the formation of these active metabolites. These results underscore the promising potential of integrating predictive modeling into food technology and highlight the need for further experimental validation and model refinement to support innovative strategies for developing healthier and more sustainable food products.

Bread and cereal-based products represent not only a source of macronutrients but also a complex food matrix containing low-molecular-weight phenolic, aromatic and fermentation-derived compounds. A number of these molecules have been associated with antioxidant, anti-inflammatory, antifungal and receptor-mediated effects in experimental studies and pharmacological databases. However, these records mainly describe test systems for individual compounds and do not demonstrate the corresponding effects directly in bread products. The objective was to develop a reproducible chemoinformatics framework for internal prioritization of compounds associated with bread and cereal matrices based on structural features and ChEMBL pharmacological annotations. The study included 43 curated compounds. After structural standardization and InChIKey deduplication, 2,058 molecular features were calculated, including physicochemical descriptors and topological fingerprints. Functional labels were generated from ChEMBL records for four activity classes: antioxidant, anti-inflammatory, antifungal, and AhR-modulating. Logistic regression, random forest, CatBoost, and XGBoost were compared using nested repeated multilabel cross-validation; no independent external dataset was used in this study. Random forest showed the best balance of performance and robustness, with a macro average precision of 0.8041 (95 % CI: 0.7766–0.8320) and a macro ROC-AUC of 0.8056. The anti-inflammatory class showed the most stable performance, whereas the antioxidant class was more heterogeneous. The highest-ranking compounds were protocatechuic, caffeic, gallic and ferulic acids, quercetin, and (+)-catechin. Evaluation with molecular scaffold-based splitting reduced macro average precision to 0.6245, indicating limited transferability to structurally distant compounds. The proposed approach should be regarded as a tool for preliminary candidate selection for further external and biological validation, rather than as experimental confirmation of their functional effects in the bread matrix.