Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two quantitative structure-activity relationship (QSAR) classification models were developed using K nearest neighbours (KNN) and neighbourhood classifier (NEC). Molecular descriptors were calculated for the data set and database compounds using ADRIANA.Code of Molecular Networks. Principal components analysis (PCA) was used to select the descriptors. The developed models were validated by leave-one-out cross validation (LOO CV). The performances of the developed models were evaluated with an external test set. Highly predictive models were used for database virtual screening. Furthermore, hit compounds were subsequently subject to molecular docking. Five hits were obtained based on consensus scoring function and binding affinity as potential HDAC8 inhibitors. Finally, HDAC8 structures in complex with five hits were also subjected to 5 ns molecular dynamics (MD) simulations to evaluate the complex structure stability. To the best of our knowledge, the NEC classification model used in this study is the first application of NEC to virtual screening for drug discovery.

The suspension stability was investigated as the function of particle number concentration, applied force, and the distance between cathode and anode. The suspension was prepared by using BaTiO3 nanoparticles in a mixed solution with ethanol and acetylacetone. The highest zeta potential value was 30 mV, when the mixing ratio of EtOH and Acac was 75 : 25. EPD conditions of BaTiO3 nanoparticles suspension with 0.05 wt% were settled at the conditions of an applied dc field of 20 V/ and an electrode distance of 1.5 at 300 seconds of deposition time.

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