Abstract
Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) based Quantitative Structure-Activity Relationships (QSARs) models were developed to predict enzymatic activities, that is, the Michaelis-Menten constant (Km) and the maximum reaction rate (Vmax) for reactions involving the biotransformation of xenobiotics, catalysed by three classes of enzymes present in the mammalian livers. The enzymes we have studied here are alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), and Flavin-containing monooxygenase (FMO). Data for enzymatic constants were collected from the literature and the computation of potential predictors was done for all xenobiotics to include for hundreds of molecular descriptors. The best predictor variables were selected (maximum of seven and a minimum of two descriptors) using the Microsoft excel correlation function for each enzyme class. Each dataset was divided into three sets, the divisions were training, cross-validation, and test sets in the ratio of 70%, 15%, and 15% respectively for both the ANNs and the MLR models to build the QSARs. The MATLAB programming language was employed to implement the writing and running of the learning algorithms. The predictive strengths of the models were assessed through the correlation of their predictions relative to the target outcomes for the three divisions and the mean square errors were computed, after fitting the resulting models with the entire dataset for each enzyme class. The ANNs model appeared best as it was seen to be relatively stable in performance through the training, cross-validation, and test sets of the data than the MLR model. For the prediction of Km, the most influential descriptors were partition coefficients and functional groups or fragments for compounds metabolised by ADH, ALDH, and FMO. Size, shape, symmetry, and atom distribution are those properties that mostly influenced the prediction of Vmax. This study is valuable in predicting Km and Vmax and for understanding the principles behind biotransformation by the liver enzymes; which in turn can be useful in taking proactive and remedial actions on issues regarding industrial activities affecting environmental wellbeing. It also finds relevance when guidance is needed for selecting an appropriate analytical model for a given dataset.
Keywords
Machine Learning, Supervised Learning, Artificial Neural Network, Multiple Linear Regression, Quantitative Structure-Activity Relationships, Xenobiotic, Michaelis-Menten Constant.
Citations
IRE Journals:
Sunday Tarekakpo Odobai , Nazifi Lawal Bashir
"Data Analytics – Computer Modelling of Metabolic Rates" Iconic Research And Engineering Journals Volume 5 Issue 8 2022 Page 116-132
IEEE:
Sunday Tarekakpo Odobai , Nazifi Lawal Bashir
"Data Analytics – Computer Modelling of Metabolic Rates" Iconic Research And Engineering Journals, 5(8)
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