Marvels of Artificial and Computational Intelligence in Life Sciences

An Introduction to Diabetes Drug Discovery in Biomedical Industry through Artificial Intelligence, Using Lichens' Secondary Metabolites

Author(s): N. Rajaprabu* and P. Ponmurugan

Pp: 22-43 (22)

DOI: 10.2174/9789815136807123010007

* (Excluding Mailing and Handling)


Proven history in science shows that natural products play a vital role in drug discovery, specifically for immune deficiencies, infectious diseases, and other therapeutic areas, including cardiovascular diseases and multiple sclerosis. Monk Agastyar and Pandit Ayothidhas contributed more to the field of Siddha through monoand polyherbal medicine and cured many diseases, including oxidative stress and diabetes. Using computational and analytical intelligence methods, this study aims to develop a natural phycobiont (lichens) edible source of metabolites for the chronic and metabolic disorder type II diabetes. The level of docking was ranked based on the iGEMDOCK grading function, with zero being the most accurate ligand. Ultimately, each complex from each fungus that ensured different binding pockets of the 6AK3 had been designated throughout the virtual screening process. Based on the uppermost energy value, the best compounds from each fungus showed accurate molecular docking. Out of the 22 compounds tested, the anthracene-9-one and acetamide found in R. conduplicans showed a high binding capacity. Meanwhile, the binding energy potential of M-Dioxan-4-ol, 2,6-dimethyl, obtained from X. curta, and 2-Chloroethyl Methyl Sulfoxide, obtained from M. fragilis, was enormous. 3, 4-13, 14-dodecahydr-18,18a-dihydroxy-2-methyl-, and 1,4-Bis (trimethylsilyl) benzene were all found in P. reticulatum.

Keywords: Binding Energy, Docking, Metformin, Orsellinic Acid, Octasiloxane, Paraldehyde, Simulation, Xylaria Curta.

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