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Mandlik, V., Bejugam, P.R. and Singh, S. (2016) Application of Artificial Neural Networks in Modern Drug Discovery. In: Puri, M., Pathak, Y., Kumar Sutariya, V., Tipparaju, S. and Moreno, W., Eds., Artificial Neural Network for Drug Design, Delivery and Disposition, Academic Press, Cambridge, 123-139.
https://doi.org/10.1016/B978-0-12-801559-9.00006-5

has been cited by the following article:

• TITLE: Development of Novel Benzimidazole Derivates as Potent and Selective Akt Kinase Inhibitors Using In-silico Approaches

  AUTHORS: Rasha Ghanem Kattoub, Faten Sliman, Mohammad Kousara

  KEYWORDS: Akt, Selective ATP-competitive Inhibitors, Benzimidazole, Molecular Docking, In-silico ADMET

  JOURNAL NAME: International Journal of Organic Chemistry, Vol.11 No.3, September 26, 2021

  ABSTRACT: The serine/threonine Akt kinase signaling pathway plays an essential role not only in tumorigenesis but also in the potential response to anticancer therapeutic agents. Therefore, aiming to identify potent and selective Akt inhibitors, a novel series of benzimidazole derivatives were designed and docked within the crystal structure of Akt1 kinase. In order to predict their selectivity, the hit compounds were docked against the protein kinase A (PKA), which is the closely related AGC family kinase protein. Moreover, in-silico ADMET-related descriptors were estimated to predict the pharmacokinetic properties for the selected compounds. Among the designed molecules, four compounds were found to have the best binding affinity and good selectivity to Akt1 kinase, furthermore, those compounds had acceptable ADMET properties and were predicted to be non-mutagenic, which could account them as promising Akt1 inhibitory agents for further investigations.