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Background: Coxsackievirus group B (CVBs) are common enteroviruses associated with several diseases from etiologically to inflammatory cardiomyopathies and constitute a severe cause of mortality in newborn resulting in severe meningitis, fulminant infection, myocarditis, and encephalitis. While Berberian (BBR) is an effective antivirus and possesses potentials of suppressing CVB replication, Zeng et al. explored a structural modification of BBR by incorporating a substituted primary amine enhance antiviral potency and safety. Based on data set from Zeng et al., we attempted to propose a QSAR model that can predict the bioactivity of unknown compounds as anti-CVB1. Results: Among many descriptors, four were selected using the Genetic Functional Approximation (GFA). Internal and external validation was carried out on data set using statistical parameters. The QSAR model was seen to meet the minimum requirement with Lack of fit = 0.068744, R2 0.897, Adjusted R2 = 0.8627, cross-validated R2 = 0.76169, R2 predicted = 0.68. Conclusion: The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds. [ABSTRACT FROM AUTHOR]

C 7 /C 8 -cyclitols and C 7 N-aminocyclitols find applications in the pharmaceutical sector as α-glucosidase inhibitors and in the agricultural sector as fungicides and insecticides. In this study, we identified C 7 /C 8 -cyclitols and C 7 N-aminocyclitols as potential inhibitors of Streptomyces coelicolor ( Sco ) GlgEI-V279S based on the docking scores. The protein and the ligand (targets 11 , 12 , and 13 ) were prepared, the states were generated at pH 7.0 ± 2.0, and the ligands were docked into the active sites of the receptor via Glide™. The synthetic route to these targets was similar to our previously reported route used to obtain 4-⍺-glucoside of valienamine ( AGV ), except the protecting group for target 12 was a p -bromobenzyl (PBB) ether to preserve the alkene upon deprotection. While compounds 11 - 13 did not inhibit Sco GlgEI-V279S at the concentrations evaluated, an X-ray crystal structure of the Sco GlgE1-V279S/ 13 complex was solved to a resolution of 2.73 Å. This structure allowed assessment differences and commonality with our previously reported inhibitors and was useful for identifying enzyme-compound interactions that may be important for future inhibitor development. The Asp 394 nucleophile formed a bidentate hydrogen bond interaction with the exocyclic oxygen atoms (C(3)-OH and C(7)-OH) similar to the observed interactions with the Sco GlgEI-V279S in a complex with AGV (PDB:7MGY). In addition, the data suggest replacing the cyclohexyl group with more isosteric and hydrogen bond-donating groups to increase binding interactions in the + 1 binding site., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Thanvi, Jayasinghe, Kapil, Obadawo, Ronning and Sucheck.)

Background: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates., Methods: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target., Results: The QSAR model of choice satisfied the recommended values and was statistically significant. The R 2 pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol -1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds., Conclusion: The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)