Using Machine Learning for the Discovery and Development of Multitarget Flavonoid-Based Functional Products in MASLD.

Area of Science:

• Computational chemistry and drug discovery
• Pharmacology and toxicology
• Nutraceutical science

Background:

• Metabolic dysfunction-associated steatotic liver disease (MASLD) requires multi-target therapies.
• Traditional approaches often focus on single markers, which is insufficient for MASLD.
• Novel computational strategies are needed for developing effective MASLD treatments.

Purpose of the Study:

• To develop and validate a fully in silico nutraceutical screening pipeline for MASLD.
• To integrate molecular prediction, systemic aggregation, and technological design for nutraceutical formulation.
• To generate rational, multi-target nutraceutical formulations for MASLD.

Main Methods:

• Assembled a panel of ten MASLD-relevant targets from proteomic evidence.
• Extracted and standardized bioactivity data from ChEMBL, generating RDKit descriptors.
• Employed stacked ensemble predictive modeling (Random Forest, XGBoost, CatBoost) with isotonic calibration.
• Utilized physiologically based pharmacokinetic (PBPK) modeling for dose and exposure estimations.
• Integrated physicochemical properties to guide formulation and delivery system design.

Main Results:

• Achieved robust predictive model performance (cross-validated ROC-AUC 0.834, test ROC-AUC 0.840).
• Generated three prototype nutraceutical concepts (HepatoBlend, LiverGuard Tea, HDL-Chews) with defined dosing and exposure profiles.
• Demonstrated successful integration of molecular prediction, systemic coverage, and formulation specifications.
• Identified six structural and twelve mechanism-of-action (MOA) clusters ensuring chemical diversity.

Conclusions:

• The in silico pipeline rationally generates multi-target nutraceutical formulations for MASLD.
• This computational framework links molecular predictions with systemic coverage and practical formulation.
• The approach provides a transferable framework for developing nutraceuticals for MASLD and other metabolic conditions.