Discovery of 3-(benzofuran-2-ylmethyl)-1H-indole derivatives as potential autophagy inducers in cervical cancer cells - Siddiqui, Shaziyaparveen K.; SahayaSheela, Vinodh J.; Kolluru, Srinivas; Pandian, Ganesh N.; Santhoshkumar, Thankayyan R.; Dan, Vipin M.; Ramana, V, Chepuri (Bioorganic & Medicinal Chemistry Letters, 2020)
In this manuscript we have documented the identification of a novel anticancer scaffold 3-(benzofuran-2-yl-methyl)-1H-indole. This scaffold has been designed by tweaking the known bisindolylmethane scaffold of natural products that display a wide range of biological activities. A series of 24 new conjugates have been synthesized and among them 5 derivatives exhibited IC50 values less than 40 mu M against two cervical cancer cell lines SiHa and C33a. Further mechanistic studies of two compounds 3eb and 3ec revealed that the toxicity of these compounds was due to the effective induction of autophagy mediated cell death. The autophagy induction was confirmed by the progressive conversion of LC3I to LC3II and downregulation of p62 in cervical cancer cells.
Keywords: Autophagy; Benzofuran; Bisindolylmethane; Cervical cancer; Friedel-Crafts alkylation

Artificial intelligence in microbial natural product drug discovery: current and emerging role - SahayaSheela, Vinodh J.; Lankadasari, Manendra B.; Dan, Vipin Mohan; Dastager, Syed G.; Pandian, Ganesh N.; Sugiyama, Hiroshi (Natural Product Reports, 2022)
Covering: up to the end of 2022 Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.