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<p>Diabetes mellitus (DM) is a growing global health challenge, particularly in low-resource settings where access to effective therapies remains limited. Dual inhibition of dipeptidyl peptidase IV (DPP-IV) and sodium–glucose co-transporter 2 (SGLT2) offers a synergistic therapeutic strategy by enhancing insulin secretion and promoting glucose excretion. This study developed an integrated in silico framework combining stacked ensemble machine learning, molecular docking, MD Simulations, and ADMET profiling to identify dual DPP-IV/SGLT2 inhibitors from <i>M. oleifera</i> metabolites. Baseline models were generated using 110 algorithm–descriptor combinations per target, and stacking significantly improved predictive accuracy, achieving Matthews Correlation Coefficients of 0.968 (training) and 0.937 (testing) for DPP-IV and 0.968 (training) and 0.861 (testing) for SGLT2. Validation against FDA-approved inhibitors confirmed the models’ reliability and generalisability. LC–MS/MS profiling of <i>M. oleifera</i> revealed several metabolites with high predicted activity, among which vitexin, homoorientin, lariciresinol 4-O-β-D-glucopyranoside, and N,α-L-rhamnopyranosyl vincosamide showed strong binding affinities and favourable pharmacokinetic properties. MD Simulations analyses further position N,α-L-rhamnopyranosyl vincosamide as a potential dual DPP-IV/SGLT2 hit inhibitor. The findings highlight <i>M. oleifera</i> as a promising source of multitarget antidiabetic compounds and demonstrate the potential of stacked ensemble learning in accelerating natural product-based drug discovery.</p>

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Stacked ensemble learning and in-silico profiling reveal dual DPP-IV and SGLT2 inhibitors from Moringa oleifera metabolites

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月18日

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  • M. K. Letuku,
  • M. G. Mohlala,
  • P. Appiah-Kubi,
  • A. Singh,
  • Y. B. Nuapia

摘要

Diabetes mellitus (DM) is a growing global health challenge, particularly in low-resource settings where access to effective therapies remains limited. Dual inhibition of dipeptidyl peptidase IV (DPP-IV) and sodium–glucose co-transporter 2 (SGLT2) offers a synergistic therapeutic strategy by enhancing insulin secretion and promoting glucose excretion. This study developed an integrated in silico framework combining stacked ensemble machine learning, molecular docking, MD Simulations, and ADMET profiling to identify dual DPP-IV/SGLT2 inhibitors from M. oleifera metabolites. Baseline models were generated using 110 algorithm–descriptor combinations per target, and stacking significantly improved predictive accuracy, achieving Matthews Correlation Coefficients of 0.968 (training) and 0.937 (testing) for DPP-IV and 0.968 (training) and 0.861 (testing) for SGLT2. Validation against FDA-approved inhibitors confirmed the models’ reliability and generalisability. LC–MS/MS profiling of M. oleifera revealed several metabolites with high predicted activity, among which vitexin, homoorientin, lariciresinol 4-O-β-D-glucopyranoside, and N,α-L-rhamnopyranosyl vincosamide showed strong binding affinities and favourable pharmacokinetic properties. MD Simulations analyses further position N,α-L-rhamnopyranosyl vincosamide as a potential dual DPP-IV/SGLT2 hit inhibitor. The findings highlight M. oleifera as a promising source of multitarget antidiabetic compounds and demonstrate the potential of stacked ensemble learning in accelerating natural product-based drug discovery.