Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis
Aziz Habibi‐Yangjeh, Mohammad Danandeh-Jenagharad · Monatshefte für Chemie - Chemical Monthly · 2009
Toxicological assessment of phenolic compounds is essential for risk-assessment purposes. Compounds with a single aromatic ring substituted with a hydroxyl group (the phenols) are ubiquitous in nature and are used in many industries including those involving textiles, leather, paper, and oil. They are also commonly used food additives and frequently utilized in agriculture [ 1 ]. There has therefore been great interest in assessing the toxicity of such compounds. The impact of the potential hazard of untested chemicals, a challenge confronting national and international regulatory agencies [ 2 – 5 ], can be measured by experimental investigations, but this approach is both quite expensive and time-consuming. This has meant that the development of computational methods as an alternative tool for predicting the properties of chemicals has been a subject of intensive study. Among computational methods quantitative structure–activity relationships (QSAR) have found diverse applications for predicting compounds’ properties, including biological activity prediction [ 6 ], physical property prediction [ 7 ], and toxicity prediction [ 8 , 9 ]. QSPR/QSAR models are essentially calibration models in which the independent variables are molecular descriptors that describe the structure of molecules and the dependent variable is the property/activity of interest. In QSAR studies, techniques which can be used for model construction, for example multiple linear regression (MLR) and artificial neural networks (ANN), have been used for inspection of linear and nonlinear relationships between the activity of interest and molecular descriptors. Artificial neural networks have become popular in QSPR/QSAR models because of their success where complex non-linear relationships exist amongst data [ 10 , 11 ]. An ANN is formed from artificial neurons connected with coefficients (weights), which constitute the neural structure and are organized in layers. The layers of neurons between the input and output layers are called hidden layers. Neural networks do not need explicit formulation of the mathematical or physical relationships of the problem handled. These give ANNs an advantage over traditional fitting methods for some chemical applications. For these reasons, in recent years ANNs have been applied to a wide variety of chemical problems [ 12 – 20 ]. Application of these techniques usually requires selection of variables to build well-fitting models. Nowadays, genetic algorithms (GA) are well-known as interesting and more widely used methods for variable selection [ 21 – 23 ]. GA are stochastic methods used to solve optimization problems defined by fitness criteria, by applying the evolution hypothesis of Darwin and different genetic functions, i.e., crossover and mutation.