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Chemoinformatics Profiling of Ionic Liquids Cytotoxicity—From Machine Learning to Network-Like Similarity Graphs
Maykel Cruz-Monteagudo * 1 , Eduardo Tejera 2 , Cesar Paz-y-Miño 2 , Yunierkis Perez-Castillo 3 , Aminael Sánchez-Rodríguez 4 , Fernanda Borges 5 , María Natália Días Soeiro Cordeiro 6
1  CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal & Instituto de Investigaciones Biomédicas (IIB), Universidad de Las Américas, 170513 Quito, Ecuador
2  Instituto de Investigaciones Biomédicas (IIB), Universidad de Las Américas, 170513 Quito, Ecuador.
3  Sección Físico Química y Matemáticas, Departamento de Química, Universidad Técnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador & Centro de Bioactivos Químicos (CBQ), Central University of Las Villas, Santa Clara, 54830, Cuba.
4  Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador.
5  CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.
6  REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.

10.3390/MOL2NET-1-b001
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-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.

Comments on this paper
Humbert González-Díaz
Are there other cellular lines used in ILs cytotoxicity tests?
Dear authors,

Firstly, I would like to thank you by your participation in MOL2NET.
I have a doubt. Do you used cytotoxicity data about only one cellular line.
Are there other cellular lines used in ILs cytotoxicity tests?
If so, are you planning to/have you already developed more models for other cellular lines?



 
 
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