Room-temperature ionic liquids (ILs) are considered green chemicals that may replace volatile organic solvents currently used by industry. However, toxicological effects of ILs are not well known. In this study, we describe the cytotoxicity of selected imidazolium-derived ILs in Caco-2 cells, prototypical human epithelial cells. The most toxic IL was 1-decyl-3-methylimidazolium chloride ([C₁₀mim][Cl]), whereas the least toxic was 1,3-dimethylimidazolium methyl sulfate ([C₁mim][MSO₄]). Using the toxicological experimental data obtained we developed a Quantitative Structure–Activity Relationship (QSAR) study using the Topological Sub-Structural Molecular Design (TOPS-MODE) approach. The model found showed excellent statistical parameters and from their interpretation, we arrived to some important conclusions such as: For 1-alkyl-3-methylimidazolium chloride derivatives, a correlation between the R₂ alkyl chain length and toxicity was observable. A positive contribution of p-chloro substituent in benzyl ring is detected among 1-methylarylimidazolium chloride derivatives. Regarding the contribution of the anion, anion chloride presents a positive contribution to toxicity whereas for compounds [C₁mim][MSO₄] and [C₁eim][ESO₄], a negative fragment contribution can be detected in anion methyl or ethyl sulfate. The EC₅₀ values determined for the ILs analysed in this study in Caco-2 cells are lower than the data reported in the literature on the toxicity of classical solvents assessed with cell lines. In conclusion, our results indicate that the possible cytotoxic effect of ILs should be considered in the design and overall evaluation of these compounds. Nevertheless, any toxicity evaluation of ILs should take their bioavailability into account, which will be affected by their low volatility, compared to conventional solvents.

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of combinatorial chemistry and high throughput screening, an unprecedented amount of experimental information has been generated. In addition, many molecular descriptors have been defined in the last two decays. All this information can be analyzed by QSAR techniques using adequate statistical procedures. These techniques and procedures should be fast, automated, and applicable to large data sets of structurally diverse compounds. For that reason, the identification of the best one seems to be a very difficult task in view of the large variable selection techniques existing nowadays. The intention of this review is to summarize some of the present knowledge concerning to variable selection methods applied to some well-known statistical techniques such as linear regression, PLS, kNN, Artificial Neural Networks, etc, with the aim to disseminate the advances of this important stage of the QSAR building model.

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.