Back-propagation neural networks (NNs) are useful for the study of quantitative structure–activity relationships or structure–property correlations. Models of relationships between structure and boiling point (bp) of 150 alkanes were constructed by means of a multilayer neural network (NN) using the back-propagation algorithm. The results of our NN were compared with those of other models from the literature, and found to be better. The boiling points of the 150 alkanes were then predicted by removing 15 compounds (test set) and using the 135 other molecules as a training set. Using the same process, all the compounds in the data bank were then predicted in groups of 15 compounds. The results obtained were satisfying.

In the present study, we used benzimidazolone as a starting material to efficiently synthesize several hybrid compounds of pyrazole benzimidazolone derivatives by the 1,3-dipolar cycloaddition reaction. These compounds were obtained in average yields and were characterized by NMR (¹H and ¹³C) and HRMS analysis. The antioxidant activity of the synthesized compounds 5(a–c) and 6(a–c) was evaluated using in vitro reduction assays, including ferric reducing antioxidant power (FRAP) and total antioxidant capacity (TAC). The results indicated that products 5c, 6b, and 6c exhibit higher antioxidant activity compared to the reference compounds and showed a remarkable ability to effectively remove the radical at IC₅₀ (14.00 ± 0.14, 12.47± 0.02, and 12.82 ± 0.10 µM, respectively) under the TAC assessment. Conversely, compound 6c showed excellent activity at IC₅₀ (68.97 ± 0.26 µM) in the FRAP assay. We carried out molecular docking and dynamics simulations to investigate the binding mode and stability of 5c, 6b, and 6c in the active site of human Peroxiredoxin 5. An ADMET study was conducted to determine the drug properties of the synthesized compounds.

Models of the relationships between structure and musk odour of tetralin and indan compounds were elaborated with a multilayer neural network using the back-propagation algorithm. The neural network was used to classify the compounds studied into two categories (musk or non-musk). The cross-validation procedure was used to assess the predictive power of the network. Each molecule was described by eight global parameters: five steric and three electronic descriptors. The neural network's results were successfully compared to those given by the k-Nearest Neighbours and the Bayesean methods, both in the classification and prediction tests. The contribution of each descriptor to the structure-odour relationships was evaluated. Three out of the eight descriptors were thus found to be the most relevant in the molecular description for the prediction of musk odour. This research points out that neural networks are likely to become a useful technique for structure-odour relationships.