The correlations between 25 features (represents the compounds detected in the <i>Artabotrys sumatranus</i> leaf extract) which had the highest positive correlation to 1/IC<sub>50</sub> AGI (α-glucosidase inhibition).

The correlations between 25 features (represents the compounds detected in the <i>Artabotrys sumatranus</i> leaf extract) which had the highest positive correlation to 1/IC<sub>50</sub> AGI (α-glucosidase inhibition).

<p>Red colors represent higher (more positive) correlation between the features, while blue color lower correlations. All 25 features had positive correlations to 1/IC<sub>50</sub> AGI, so that they all had potentials to be active compounds as α-glucosidase inhibitors.</p>

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Bibliographic Details
Main Author:	Dela Rosa (20501630) (author)
Other Authors:	Berna Elya (3969104) (author), Muhammad Hanafi (18169031) (author), Alfi Khatib (1817224) (author), Eka Budiarto (20501633) (author), Syamsu Nur (20501636) (author), Muhammad Imam Surya (20501639) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Genetics Pharmacology Sociology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified shapley score calculation saccharomyces cerevisiae </ postprandial glucose intake molecular docking simulation make predictive model div >< p artabotrys sumatranus </ predicted active compound one predicted compound multivariate statistical analysis molecular docking analysis feature importance analysis bioassay guided fractionation low positive correlation machine learning methods machine learning approaches predicted active compounds >, usually used showed strong binding glucosidase inhibition activity showed strong α machine learning active compounds showed strong strong binding correlation analysis strong correlation one way metabolomics analysis combined analysis bioassay test glucosidase inhibitors glucosidase inhibitor glucosidase enzyme glucosidase activity yet reported verification ). using α random forest performance comparisons metabolomics prediction isolation based important features human gut biosynthesis pathway antioxidant activity another result also reflected activity prediction 3 groups
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