Ionic liquids (ILs) constitute one of the hottest areas in chemistry since they have become increasingly popular as reaction and extraction media. Their almost limitless structural possibilities, as opposed to limited structural variations within molecular solvents, make ILs "designer solvents." They also have been widely promoted as "green solvents" although their claimed relative nontoxicity has been frequently questioned. The Thinking in Structure-Activity Relationships (T-SAR) approach has proved to be an efficient method to gather relevant toxicological information of analog series of ILs. However, when data sets significantly grow in size and structural diversity, the use of computational models becomes essential. We provided such a computational solution in a previous work by introducing a reliable, predictive, simple, and chemically interpretable Classification and Regression Tree (CART) classifier enabling the prioritization of ILs with a favorable cytotoxicity profile. Even so, an efficient and exhaustive mining of SAR information goes beyond analog compound series and the applicability domain of quantitative SAR modeling. So, we decided to complement our previous findings based on the use of the CART classifier by applying the network-like similarity graph (NSG) approach to the mining of relevant structure-cytotoxicity relationship (SCR) trends. Finally, the SCR information concurrently gathered by both, quantitative (CART classifier) and qualitative (NSG) approaches was used to design a focused combinatorial library enriched with potentially safe ILs.
Keywords: SAR mining; SAR pathways; SAR trees; cytotoxicity; focused combinatorial library design; ionic liquids; network similarity graphs; structure-cytotoxicity relationships..