Technical Computational Approach for Drug Discovery: in silico Analysis of Non-Edible Parts of Durio oxleyanus from Sabah as Enzyme Inhibitors in Diabetes Management - Qomariah Hasanah; Monica Suleiman; Ayu Indayanti Ismail; Elfira Jumrah; Nur Hasanah; Syahriel bin Abdullah (Journal of Educational Technology and Learning Creativity, 2026)
Purpose of the study: One strategy for controlling blood glucose levels is to inhibit carbohydrate digestion enzymes, namely α-glucosidase and α-amylase. This study uses a technical computational approach to explore the bioactive potential of the inedible part (husk) of Durio oxleyanus from Sabah as an inhibitor of both enzymes. Methodology: Hydroalcoholic extracts were analyzed using LC-MS, which identified phenolic and flavonoid compounds including catechin, rutin, quercetin, malvidin-3-O-glucoside, and lucidenic acid D1. Molecular docking simulations were performed against α-glucosidase (PDB ID: 2QMJ) and α-amylase (PDB ID: 1B2Y) using PyRx integrated with AutoDock and AutoDock Vina. Main Findings: The results showed that several compounds had high binding affinity (ΔG ≤ -7.0 kcal/mol) and interacted stably with polar, aromatic, and hydrophobic residues in the enzyme's active site. Lucidenic acid D1, rutin, catechin, and quercetin-3-O-glucosidase stood out as the main candidates with the lowest binding energy and multipoint interactions that support the formation of stable complexes. Novelty/Originality of this study: This study has demonstrated significant innovation by revealing the potential of the inedible parts of Durio oxleyanus as a source of candidate antidiabetic drugs through a computational approach. The integration of LC-MS profiling and molecular docking enables the efficient identification of bioactive compounds while accelerating the drug discovery process without relying entirely on experimental methods. This research lies in its contribution to the concept of sustainable bioprospecting, the exploration of under-researched local biodiversity, and the multi-target validation of key diabetes enzymes.