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The aim of this work was to obtain an inorganic oxide system containing silica and magnesium oxide, and characterized by specific physicochemical properties, in particular well-defined adsorption parameters. The preparation process was carried out according to a co-precipitation method using solutions of sodium silicate and selected inorganic magnesium salt. The oxide system obtained (MgOÁSiO 2 ) was used as a support (adsorbent) of nickel(II) ions, whose precursors were model solutions of nitrates. The effectiveness of the adsorption process was evaluated using many different analytical techniques, including atomic absorption spectroscopy, energy dispersive X-ray spectroscopy and equivalent point titration. Moreover the stability of adsorbent/adsorbate bonding was estimated. The oxide systems-adsorbents-used in the process were also analyzed according to their physicochemical properties, especially changes in adsorption parameters. The last part of the study involved evaluation of the kinetics of the adsorption process depending on time and the pH of the reaction system.

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The paper presents a comprehensive overview of the use of artificial intelligence (AI) systems in drug design. Neural networks, which are one of the systems employed in AI, are used to identify chemical structures that can have medical relevance. Successful training of neural networks must be preceded by the acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity. In general, a neural network requires a large set of training data, which must contain information about the chemical structure-biological activity relationship.The data can come from experimental measurements, but can also be generated using appropriate quantum models. In many of the studies presented below, authors showed a significant potential of neural networks to produce generalizations based on even relatively narrow training data. Despite the fact that neural network systems have been known for more than 40 years, it is only recently that they have seen rapid development due to the wider availability of computing power. In recent years, there has been a growing interest in deep learning techniques, bringing network modeling to a new level of abstraction. Deep learning allows combining what seems to be causally distant phenomena and effects, and to associate facts in a way resembling the human mind.

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