A series of some 3d (Mn, Fe, Co, Ni, Cu and Zn) metal(II) complexes of mixed-ligands-N,N′-dimethyldithiocarbamate (SDTC) and 4,4,4-trifluoro-1-(2-naphthyl)-1,3-butanedione (TFNB) have been synthesized and presented for antioxidant, antibacterial and antifungal studies. The complexes were examined for their in-vitro antioxidant potentials by employing ferrous ion chelating and DPPH scavenging methods, while the in-vitro antimicrobial screening of the unbound ligands and their mixed metal(II) complexes against bacteria-Staphylococcus aureus, Bacillus cereus (Gram (+ve)), Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli (Gram (−ve)), and fungi-Aspergillus niger, Aspergillus flavous, Fusarium species using agar and disc diffusion methods respectively. The studies revealed that Co(II) complex had the highest antioxidant potential with the percentage scavenging inhibition of 75.04%. The antimicrobial screening of the complexes against the bacterial microbes showed that Cu(II) complex had the best activity with inhibitory zone range of 19.7-31.3 mm; while Fe(II) complex possesses highest fungicidal activities within the inhibitory zone range of 28.7-38.0 mm; compared to other complexes against the fungi strains. Molecular docking approach indicated that Cu(II) complex had higher binding affinity with π-sulphur and Van der Waals interactions.

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Background The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl esters and probed into the mode of interaction of these molecules with an epidermal growth factor receptor (3POZ) using the three-dimensional quantitative structure activity relationship (QSAR), extreme learning machine (ELM), and molecular docking techniques. Results The developed QSAR model with predicted (R2pred) of 0.756 showed that the model was fit to be validated parameter for a built model and also proved that the developed model could be used in practical situation, R2 for training set (0.9929) and test set (0.8397) confirmed that the model could successfully predict the activity of new compounds due to its correlation with the experimental activity, the models generated with ELM models showed improved prediction of the activity of the molecules. The lead compounds (22 and 23) had binding energies of −6.327 and −7.232 kcalmol−1 for 22 and 23 respectively and displayed better inhibition at the binding sites of 3POZ when compared with that of the standard drug, chlorambucil (−6.0 kcalmol−1). This could be attributed to the presence of double bonds and the α-ester groups. Conclusion The QSAR and ELM models had good prognostic ability and could be used to predict the bioactivity of novel anti-proliferative drugs.

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 potentials 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 R2pred (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 MDA-MB-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.

Solar distiller was constructed and tested in this study. The purpose is to get a portable water from nearly any source available in a relatively cheaper means using a renewable solar energy. The result obtained clearly confirmed the reliability of this method to provide portable water especially in a rural area of developing country like Nigeria where the supply of fresh water is inadequate. A local dirty stream that is constantly throughout the year served as the source of the brackish water was used for this work. Sample taken from this stream was distilled using the constructed double slope solar distiller. The incoming solar radiation from the sun is focused and concentrated on to solar water distillation unit. Analyzing the sample of the distillate, th e pH value of the brackish feed water was 9.20 ±1.10 while that of the distillate was 8.10 ±1.06, which falls within the WHO limits of 6.5-8.5 for drinkable water.