Abstract

CRUZ MONTEAGUDO, Maykel; DOMINGUEZ LOPEZ, Rotceh; CESPEDES PEREZ, Ariel  and  PEREZ GUZMAN, Ricardo Enrique. Evaluation of the Neurotoxic Profile of Ionic Liquids Based using Supervised Machine Learning Techniques. Rev cuba cienc informat [online]. 2017, vol.11, n.3, pp.127-143. ISSN 2227-1899.

ABSTRACT The enzyme Acetylcholinesterase (AChE) plays an essential role in the hydrolysis of the neurotransmitter Acetylcholine, which is responsible for the transmission of nerve impulses. Since the 1930s, specialists in the chemical sciences have produced compounds that are able to inhibit this enzyme and therefore affect the transmission process of nerve impulses, which causes serious consequences for the affected organism. Current studies have shown that some ionic liquids can inhibit AChE enzyme function and cause damage to the central nervous system. Ionic liquids due to their physical-chemical characteristics are widely used in the production of solvents that are used in the substitution of molecular toxic solvents for the environment. In correspondence to this arises the need to evaluate the neurotoxic profile of ionic liquids using the AChE enzyme as an indicator of neurotoxicity. In the development of the work multiclassifiers were applied as supervised learning techniques, and as a result models were obtained capable of predicting if a new ionic liquid is able to inhibit AChE. Bagging, Boosting, Stacking and Vote multiclassifiers were used in the experimentation to identify predictive QSAR models. Five measures of diversity were calculated for the base classifiers used in Stacking and Vote multiclassifiers. Finally, two models were obtained that surpassed the performance of the individual classifiers used, reason why they were selected to solve the problem. The multiclassifier AdaBoostM1, which uses a Multilayer Perceptron neural network as the base classifier and the Stacking multiclaser, which uses the combination of classifiers FDLA, Jrip, Kstar, NaiveBayes and SMO as base classifiers, were the multiclasifiers selected.

Keywords : Acetylcholinesterase; ionic liquids; QSAR; ensembles; diversity measures.

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