Chemoinformatics Profiling of Ionic Liquids Cytotoxicity—From Machine Learning to Network-Like Similarity Graphs

Abstract

Ionic liquids (ILs) possess a unique physicochemical profile providing a wide range of applications. However, their "greenness", specifically their claimed relative non toxicity has been frequently questioned, hindering their REACH registration processes and so, their final application. In this work we introduce a reliable, predictive, simple and chemically interpretable classification and regression tree (CART) classifier enabling the prioritization of ILs with a favourable cytotoxicity profile. By inspecting the structure of the CART several moieties that can be regarded as "cytotoxicophores" were identified and used to establish a set of SAR trends specifically aimed to prioritise low cytotoxicity ILs. We also demonstrated the suitability of the joint use of the CART classifier and a group fusion similarity search as a virtual screening strategy for the automatic prioritisation of safe ILs disperse in a data set of ILs of moderate to very high cytotoxicity. Additionally, we decided to complement the quantitative results already obtained by applying the network-like similarity graphs (NSG) approach to the mining of relevant structure-SciForum Mol2Net, 2015, 1(Section B), pages 1-9, Proceedings 2 http://sciforum.net/conference/mol2net-1 cytotoxicity relationships (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.