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The knowledge of the capacity of a data set to be modeled in the first stages of the building of quantitative structure-activity relationship (QSAR) prediction models is an important issue because it might reduce the effort and time necessary to select or reject data sets and in refining the data set's composition. The modelability index (MODI) is based on the counting of the first nearest neighbor belonging to the molecules of the data set and is a standardized measurement assumed in the QSAR community. In this paper, we revisit the calculation of the modelability index, proposing a more formal formulation that extends the calculation to the first nearest neighbors that belong to each existing class in the data set. In addition, this new formulation allows the calculation of the rivality index, as a measurement of the presence of correctly classifiable molecules and activity cliffs. By weighting the rivality index considering the cardinality of the neighborhood of each molecule of the data set, the calculated weighted modelability index is highly correlated with the correct classification rate (QSAR_CCR) obtained in the building of QSAR models using different classification algorithms. The results obtained with the weighted modelability index show correlations of r higher than 0.9, slopes close to 1, and bias close to zero for different algorithms.

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The current social impact of new technologies has produced major changes in all areas of society, creating the concept of a smart city supported by an electronic infrastructure, telecommunications and information technology. This paper presents a review of Bluetooth Low Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their use and influence within different areas of the development of the smart city. The document also presents a review of Big Data Solutions for the management of information and the extraction of knowledge in an environment where things are connected by an “Internet of Things” (IoT) network. Lastly, we present how these technologies can be combined together to benefit the development of the smart city.

An unambiguous algorithm, added to the study of the applicability domain and appropriate measures of the goodness of fit and robustness, represent the key characteristics that should be ideally fulfilled for a QSAR model to be considered for regulatory purposes. In this paper, we propose a new algorithm (RINH) based on the rivality index for the construction of QSAR classification models. This index is capable of predicting the activity of the data set molecules by means of a measurement of the rivality between their nearest neighbors belonging to different classes, contributing with a robust measurement of the reliability of the predictions. In order to demonstrate the goodness of the proposed algorithm we have selected four independent and orthogonally different benchmark data sets (balanced/unbalanced and high/low modelable) and we have compared the results with those obtained using 12 different machine learning algorithms. These results have been validated using 20 data sets of different balancing and sizes, corroborating that the proposed algorithm is able to generate highly accurate classification models and contribute with valuable measurements of the reliability of the predictions and the applicability domain of the built models.