The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms. The conversion of DBT and selectivity was estimated by applying Lasso, Ridge, and Random Forest regression techniques. For the estimation of conversion of DBT, Random Forest and Lasso offer adequate predictions. At the same time, regularized regressions have similar outcomes, which are suitable for selectivity estimations. According to the regression coefficient, the structural parameters are essential predictors for selectivity, highlighting the pore size, and slab length. These properties can connect with aspects like the availability of active sites. The insights gained through ML techniques about the HDS catalysts agree with the interpretations of previous experimental reports.

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure–toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R²) and a leave-one-out coefficient (Q²_LOO) of 0.6584 and 0.6289, respectively.

Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care.

2D‐hybrid halide perovskites are semiconductor materials with excellent optical properties that have been widely studied due to their potential as materials applicable to green energy production. Data mining techniques are powerful tools to recognize and extract relevant patterns from databases. In this study, data mining techniques are used to extract relationships among geometric characteristics, composition, and the bandgap of 2D‐hybrid halide perovskites. Our analysis reveals patterns that connect the chemical properties of the organic spacer cation with the bandgap, like the distance between the halogen in the perovskite and the terminal nitrogen of the interlayer organic cation, and the relation among the type of organic interlayer cation, the interlayer distance, and the perovskite layer phase. Furthermore, it is found that aromatic cations lead to bandgaps between 2.2 and 2.4 eV. These results are consistent with previous experimental reports and provide insight into the structure–property relationships, thus illustrating the utility of data mining techniques for extracting valuable knowledge to optimize and design new materials with improved properties.