Consensus In Silico Pharmacodynamic Screening of Twelve Fabaceae Isoflavones Targeting Glycation, Oxidative Stress, Antidiabetic Activity, and NF-κB-Related Inflammation

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disorder characterized by chronic hyperglycemia, oxidative stress, protein glycation, low-grade inflammation, and dysregulation of signaling pathways, including those involving nuclear factor kappa B (NF-κB). Isoflavones from Fabaceae species exhibit diverse biological activities and may represent multifunctional candidates for addressing the interconnected mechanisms underlying T2DM. This study performed a consensus in silico pharmacodynamic screening of twelve representative Fabaceae isoflavones to prioritize compounds with predicted antiglycating, deglycating, antidiabetic, antioxidant, and NF-κB-related anti-inflammatory activities. Methods: Twelve representative Fabaceae isoflavones were evaluated using a multimethod computational workflow. Antiglycating and deglycating activities were predicted using IT Microcosm, antidiabetic and hypoglycemic activities were assessed using Prediction of Activity Spectra for Substances (PASS), antioxidant potential was evaluated using similarity-based prediction and quantum-chemical calculations, and NF-κB-related anti-inflammatory activity was investigated using similarity-based prediction combined with molecular docking. Molecular docking was applied specifically to the NF-κB-related anti-inflammatory analysis and did not contribute to the hypoglycemic/antidiabetic prediction. The individual predictions were integrated using a consensus scoring strategy to prioritize compounds across multiple pharmacodynamic domains. Results: The investigated isoflavones demonstrated moderate but broad predicted multifunctional activities. Malonylgenistin, biochanin A, and formononetin showed the most favorable predicted antiglycating profiles. Genistin, acetyldaidzin, acetylglycitin, and puerarin ranked highest in terms of predicted antidiabetic activity, whereas genistein exhibited the strongest predicted antioxidant profile. Daidzin and genistin received the highest integrated NF-κB-related computational rankings, although these predictions apply to intact glycosides rather than their expected in vivo hydrolysis products. Conclusions: This study presents a consensus computational framework for the comparative prioritization of Fabaceae isoflavones based on their predicted multifunctional pharmacodynamic profiles. The results provide a rational basis for selecting candidate compounds for future biochemical, cell-based, pharmacokinetic, and in vivo investigations, although it is emphasized that the present findings are based exclusively on computational predictions and require experimental validation.