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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The paper presents a new model for kinetically controlled adsorption at the fluid/fluid interface. The main purpose of the presented approach is to relate easy to estimate bulk surfactant concentration with Gibbs surface excess. Two adsorption isotherms are involved in the new model development: Frumkin and Szyszkowski isotherms. Additionally the Johannsen time profile of concentration in the adsorption layer is assumed and estimated in the model derivation. The proposed approach assumes the near interface, adsorptive layer which is described based on Fick’s transient diffusion law. The solution to the model contains the estimation of effective diffusivities with adsorptive layer thickness as well. The experimental results of toluene/water + sodium dodecyl sulfate are presented and used for model verification.

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The aim of this study was to evaluate the performance of zinc oxide nanoparticles as a photocatalyst for photodegradation of two model non-ionic surfactants (Triton X-100 and C12E10). The first part of the investigation was focused on the synthesis and characterization of ZnO nanoparticles, since its crystalline structure strongly impacts its photocatalytic properties. Based on the results of the XRD analysis, it was concluded that the obtained material occurred in the form of hexagonal wurtzite with a polycrystalline structure. FT-IR and XPS analyses were used to elucidate and confirm the nanomaterial structure, whereas investigation of N2 adsorption/desorption and SEM/TEM imaging allowed to establish that the synthesized ZnO was characterized as a mesoporous material with uniform, spherical shape and particle size fluctuating between 90 and 130 nm. The second part of the study included spectrophotometric assessment of the photodegradation process. The use of the obtained ZnO nanoparticles allowed to achieve efficient photodegradation of both C12E10 (92%) and Triton X-100 (82%) after 1 h of UV irradiation. The Langmuir–Hinshelwood mechanism was used to describe the reaction kinetics. Subsequent LC-MS/MS analysis of the residues indicated that the degradation mechanism is most likely based on both central fission of the surfactant molecules with further terminal oxidation of poly(ethylene glycol) and terminal oxidation leading to carboxylic derivatives of surfactants.

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