QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC–TOF‐MS

Abstract

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN.

Advanced QSRR Modeling of Organic Pollutants in Natural Water and Wastewater in Gas Chromatography Time-of-Flight Mass Spectrometry

“…where r is a constant that can be determined by considering the process to be predicted (here r was set to be 1), m is the dimension of the input space and 2  is the variance of the data [34]. It means that the value of c depends on the system under the study.…”

QSRR Approach: Application to Retention Mechanism in Liquid Chromatography

“…Over the past decade, there has been an impressive increase in the number of publications on QSRR studies that used ANN as a modeling technique. In particular, the singlehidden layer neural nets provided a satisfactory level of prediction accuracy [46][47][48][49][50][51]. After the improvement in computer power and the rise of big data, ANNs began to flourish in the form of deep learning (DL) algorithms [52][53][54][55].…”

Quantitative structure–retention relationships applied to liquid chromatography gradient elution method for the determination of carbonyl-2,4-dinitrophenylhydrazone compounds

“…Non-linear methods such as Artificial Neural Networks (ANN) have already been applied in QSRR [2,[34][35][36]. Despite non-linearity makes obtaining a model equation impossible, it also allows to found a broadly correlation patterns between variables than PLS.…”