Simultaneous clustering analysis with molecular docking in network pharmacology for type 2 antidiabetic compounds

Nur Azizah Komara Rifai(1*), Farit Mochamad Afendi(2), I Made Sumertajaya(3)

(1) Department of Statistics, Bogor Agricultural University, Jalan Meranti Wing 22 Level 4, Kampus IPB Darmaga, Bogor 16680, Indonesia
(2) Department of Statistics, Bogor Agricultural University, Jalan Meranti Wing 22 Level 4, Kampus IPB Darmaga, Bogor 16680, Indonesia
(3) Department of Statistics, Bogor Agricultural University, Jalan Meranti Wing 22 Level 4, Kampus IPB Darmaga, Bogor 16680, Indonesia
(*) Corresponding Author


The database of drug compounds and human proteins plays a very important role in identifying the protein target and the compound in drug discovery. Recently, a network pharmacology approach was established by updating the research paradigm from the current “one disease-one target-one drug” to a new “drug-target-disease network”. Ligand-protein interactions can be analyzed quantitatively using simultaneous clustering and molecular docking. The docking method offers the ability to quickly and cheaply predict the ligand-protein binding free energy (DG) in structure-based virtual screening. Meanwhile, simultaneous clustering was used to find subgroups of compounds that exhibit a high correlation with subgroups of target proteins. This study is focused on the interaction between the 306 compounds from medicinal plants (brotowali Tinospora crispa, ginger Zingiber officinale, pare Momordica charantia, sembung Blumea balsamifera, synthetic drugs (FDA-approved) and the 21 significant human proteins associated with type 2 diabetes. We found that brotowali (B018), sembung (S031), pare (P231), and ginger (J036, J033) were close to the synthetic drugs and can possibly be developed as antidiabetic drug candidates. Likewise, the proteins AKT1, WFS1, APOE, EP300, PTH, GCG, and UBC which assemble each other and which have a high association with INS can be seen as target proteins that play a role in type 2 diabetes.


Molecular docking; network pharmacology; simultaneous clustering analysis; type 2 diabetes

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