Artificial neural networks model

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Intended Use The intended use of the model sets the sophistication required. Relational models are adequate for control within narrow bands of setpoints. Physical models are reqiiired for fault detection and design. Even when relational models are used, they are frequently developed bv repeated simulations using physical models. Further, artificial neural-network models used in analysis of plant performance including gross error detection are in their infancy. Readers are referred to the work of Himmelblau for these developments. [For example, see Terry and Himmelblau (1993) cited in the reference list.] Process simulators are in wide use and readily available to engineers. Consequently, the emphasis of this section is to develop a pre-liminaiy physical model representing the unit. 

Chen et al. [24] provide a good review of Al techniques used for modeling environmental systems. Pongracz et al. [25] presents the application of a fuzzy-rule based modeling technique to predict regional drought. Artificial neural networks model have been applied for mountainous water-resources management in Cyprus [26] and to forecast raw-water quality parameters for the North Saskatchewan River [27]. 

Iliadis LS, Maris E (2007) An artificial neural network model for mountainous water-resources management the case of Cyprus mountainous watersheds. Environ Modell Softw 22 1066-1072... 

Hoskins, J. C., and Himmelblau, D. M., Artificial neural network models of knowledge representation in chemical engineering. Comput. Chem. Eng. 12, 881 (1988). 

High quality of property predictions by Molconn-Z and artificial neural network modeling. Ahstr. Papers Am. Chem. Soc. 2000, 220, U288. 

Jalali-Heravi, M., Fatemi, M. H. J. Chromatogr. A 915, 2001, 177-183. Artificial neural network modeling of Kovats retention indices for noncyclic and monocyclic terpenes. 

Garg P, Verma J (2006) In silico prediction of blood-brain barrier permeability An artificial neural network model. J Chem Inf Model 46 289-297... 

Recently, Jung et al. [42] developed two artificial neural network models to discriminate intestinal barrier-permeable heptapeptides identified by the peroral phage display experiments from randomly generated heptapeptides. There are two kinds of descriptors one is binary code of amino acid types (each position used 20 bits) and the other, which is called VHSE, is a property descriptor that characterizes the hydrophobic, steric, and electronic properties of 20 coded amino acids. Both types of descriptors produced statistically significant models and the predictive accuracy was about 70%. 

Fig. 7. Artificial neural network model. Bioactivities and descriptor values are the input and a final model is the output. Numerical values enter through the input layer, pass through the neurons, and are transformed into output values the connections (arrows) are the numerical weights. As the model is trained on the Training Set, the system-dependent variables of the neurons and the weights are determined.

Sun et al. [74] employed a 3-layer artificial neural network model with a back-propagation error algorithm to classify wine samples in 6 different regions based on the measurements of trace amounts of B, V, Mn, Zn, Fe, Al, Cu, Sr, Ba, Rb, Na, P, Ca, Mg and K by ICP-OES. Similarly,... 

J.C. Hoskins and D.M. Himmelblau. Artificial neural networks models of knowledge representation in chemical engineering. Computers and Chemical Engineering, 12 881-890,1988. 

Chen VCP, Rollins DK (2000), Issues regarding artificial neural network modeling for reactors and fermenters, Bioprocess. Eng. 22 85-93. 

The cooperative relationship between the structural features of molecules and many physiological processes makes artificial neural network models a frequent choice for predicting the ADMET properties of drug candidates. ... 

Agatonovic-Kustrin, S. Beresford, R. Basic concepts of artificial neural network modelling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 2000, 22, 717-727. 

Description A QSAR toolkit with descriptor generation (topological, geometrical, electronic, and physicochemical descriptors), variable selection, regression and artificial neural network modelling. 

Devillers J. Artificial neural network modeling in environmental toxicology. In Livingstone DJ, editor, Neural networks Methods and applications. Totowa Humana Press, 2007. 

Plasma area under the concentration—time curves (AUCs) of 57 NCEs were determined following oral cassette administration (5—9 NCEs/cassette) to mice. Physicochemical properties [such as, molecular weight, calculated molar refractivity, and calculated lipophilicity (clogP)] and molecular descriptors [such as presence or absence of N-methylation, cyclobutyl moiety, or heteroatoms (non-C,H,0,N)] were calculated or estimated for these compounds. This structural data, along with the corresponding pharmacokinetic parameters (primarily AUC), were used to develop artificial neural network models [8]. These models were used to predict the AUCs of compounds under synthesis [10]. This approach demonstrates that predictive models could be developed which potentially predict in vivo pharmacokinetics of NCEs under synthesis. Similar examples have been reported elsewhere [11—13]. 

Thermal processing of food by conduction heating Simultaneous minimization of surface cook values (i.e. maximization of final product quality) and minimization of processing time. GA An artificial neural network model was developed based on simulated data from the first principles model, and then used in optimization. Chen and Ramaswamy (2002)... 

Dielectric barrier discharge reactor for conversion of methane and CO2 into synthesis gas and C2+ hydrocarbons Three cases (a) maximization of metiiane conversion and C2+ selectivity, (b) maximization of methane conversion and C2+ yield, and (c) maximization of methane conversion and H2 selectivity. Weighted sum of squared objective functions method along with GA An artificial neural network model of the process was developed based on experimental data, and then used for optimization. Istadi and Amin (2006)... 

A.R. Khataee, Photocatal3dic removal of C.I. Basic Red 46 on immobilized Ti02 nanoparticles Artificial neural network modeling . Environmental Technology, 30, 2009, 1155-1168... 

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