Open access

Applications of Artificial Neural Networks in Chemical Problems

Written By

Vinícius Gonçalves Maltarollo, Káthia Maria Honório and Albérico Borges Ferreira da Silva

Submitted: May 31st, 2012 Published: January 16th, 2013

DOI: 10.5772/51275

Chapter metrics overview

5,181 Chapter Downloads

View Full Metrics

1. Introduction

In general, chemical problems are composed by complex systems. There are several chemical processes that can be described by different mathematical functions (linear, quadratic, exponential, hyperbolic, logarithmic functions, etc.). There are also thousands of calculated and experimental descriptors/molecular properties that are able to describe the chemical behavior of substances. In several experiments, many variables can influence the chemical desired response [1,2]. Usually, chemometrics (scientific area that employs statistical and mathematical methods to understand chemical problems) is largely used as valuable tool to treat chemical data and to solve complex problems [3-8].

Initially, the use of chemometrics was growing along with the computational capacity. In the 80’s, when small computers with relatively high capacity of calculation became popular, the chemometric algorithms and softwares started to be developed and applied [8,9]. Nowadays, there are several softwares and complex algorithms available to commercial and academic use as a result of the technological development. In fact, the interest for robust statistical methodologies for chemical studies also increased. One of the most employed statistical methods is partial least squares (PLS) analysis [10,11]. This technique does not perform a simple regression as multiple linear regression (MLR). PLS method can be employed to a large number of variables because it treats the colinearity of descriptors. Due the complexity of this technique, when compared to other statistical methods, PLS analysis is largely employed to solve chemical problems [10,11].

We can cite some examples of computational packages employed in chemometrics and containing several statistical tools (PLS, MLR, etc.): MATLAB [12], R-Studio [13], Statistica [14] and Pirouette [15]. There are some molecular modeling methodologies as HQSAR [16], CoMFA [17-18], CoMSIA [19] and LTQA-QSAR [20] that also use the PLS analysis to treat their generated descriptors. In general, the PLS method is used to analyse only linear problems. However, when a large number of phenomena and noise are present in the calibration problem, the relationship becomes non-linear [21]. Therefore, artificial neural networks (ANNs) may provide accurate results for very complex and non-linear problems that demand high computational costs [22,23]. One of the most employed learning algorithm is the back-propagation and its main advantage is the use of output information and expected pattern to error corrections [24]. The main advantages of ANN techniques include learning and generalization ability of data, fault tolerance and inherent contextual information processing in addition to fast computation capacity [25]. It is important to mention that since 90’s many studies have related advantages of applying ANN techniques when compared to other statistical methods [23,26-31].

Due to the popularization, there is a large interest in ANN techniques, in special in their applications in various chemical fields such as medicinal chemistry, pharmaceutical, theoretical chemistry, analytical chemistry, biochemistry, food research, etc [32-33]. The theory of some ANN methodologies and their applications will be presented as follows.


2. Artificial Neural Networks (ANNs)

The first studies describing ANNs (also called perceptron network) were performed by McCulloch and Pitts [34,35] and Hebb [36]. The initial idea of neural networks was developed as a model for neurons, their biological counterparts. The first applications of ANNs did not present good results and showed several limitations (such as the treatment of linear correlated data). However, these events stimulated the extension of initial perceptron architecture (a single-layer neural network) to multilayer networks [37,38]. In 1982, Hopfield [39] described a new approach with the introduction of nonlinearity between input and output data and this new architecture of perceptrons yielded a good improvement in the ANN results. In addition to Holpfield’s study, Werbos [40] proposed the back-propagation learning algorithm, which helps the ANN popularization.

In few years (1988), one of the first applications of ANNs in chemistry was performed by Hoskins et al. [41] that reported the employing of a multilayer feed-forward neural network (described in Session 2.1) to study chemical engineering processes. In the same year, two studies employing ANNs were published with the aim to predict the secondary structure of proteins [42,43].

In general, ANN techniques are a family of mathematical models that are based on the human brain functioning. All ANN methodologies share the concept of “neurons” (also called “hidden units”) in their architecture. Each neuron represents a synapse as its biological counterpart. Therefore, each hidden unity is constituted of activation functions that control the propagation of neuron signal to the next layer (e.g. positive weights simulate the excitatory stimulus and negative weights simulate the inhibitory ones). A hidden unit is composed by a regression equation that processes the input information into a non-linear output data. Therefore, if more than one neuron is used to compose an ANN, non-linear correlations can be treated. Due to the non-linearity between input and output, some authors compare the hidden unities of ANNs like a “black box” [44-47]. Figure 1 shows a comparison between a human neuron and an ANN neuron.

Figure 1.

(A) Human neuron; (B) artificial neuron or hidden unity; (C) biological synapse; (D) ANN synapses.

The general purpose of ANN techniques is based on stimulus–response activation functions that accept some input (parameters) and yield some output (response). The difference between the neurons of distinct artificial neural networks consists in the nature of activation function of each neuron. There are several typical activation function used to compose ANNs, as threshold function, linear, sigmoid (e.g. hyperbolic tangent), radial basis function (e.g. gaussian) [25,44-48]. Table 1 illustrates some examples of activation functions.

Different ANN techniques can be classified based on their architecture or neuron connection pattern. The feed-forward networks are composed by unidirectional connections between network layers. In other words, there is a connection flow from the input to output direction. The feedback or recurrent networks are the ANNs where the connections among layers occur in both directions. In this kind of neural network, the connection pattern is characterized by loops due to the feedback behavior. In recurrent networks, when the output signal of a neuron enter in a previous neuron (the feedback connection), the new input data is modified [25,44-47].

threshold linear hyperbolic tangent gaussian
φ ( r )   =   { 1 ifn 0 ;   0  ifn 0 }
φ ( r )   =   { 1 i f v 0 0 i f v   0
φ ( r )   =   { 1 r ½ ; n ½ n ½ ; 0 n ½ }
φ ( v )   = { 1 v 1 2 v 1 2    v    1 2 0 v 1 2
φ ( r )   =  tanh ( n / 2 )   =   1 exp ( n ) / 1 + exp ( n )
φ ( v )   =  tanh ( v 2 ) = 1 exp ( v ) 1 + exp ( v )
φ ( r ) = e ( ε v ) ^ 2

Table 1.

Some activation functions used in ANN studies.

Each ANN architecture has an intrinsic behavior. Therefore, the neural networks can be classified according to their connections pattern, the number of hidden unities, the nature of activation functions and the learning algorithm [44-47]. There are an extensive number of ANN types and Figure 2 exemplifies the general classification of neural networks showing the most common ANN techniques employed in chemistry.

Figure 2.

The most common neural networks employed in chemistry (adapted from Jain & Mao, 1996 [25]).

According to the previous brief explanation, ANN techniques can be classified based on some features. The next topics explain the most common types of ANN employed in chemical problems.

2.1. Multilayer perceptrons

Multilayer perceptrons (MLP) is one of the most employed ANN algorithms in chemistry. The term “multilayer” is used because this methodology is composed by several neurons arranged in different layers. Each connection between the input and hidden layers (or two hidden layers) is similar to a synapse (biological counterpart) and the input data is modified by a determined weight. Therefore, a three layer feed-forward network is composed by an input layer, two hidden layers and the output layer [38,48-50].

MLP is also called feed-forward neural networks because the data information flows only in the forward direction. In other words, the produced output of a layer is only used as input for the next layer. An important characteristic of feed-forward networks is the supervised learning [38,48-50].

The crucial task in the MLP methodology is the training step. The training or learning step is a search process for a set of weight values with the objective of reducing/minimizing the squared errors of prediction (experimental x estimated data). This phase is the slowest one and there is no guarantee of minimum global achievement. There are several learning algorithms for MLP such as conjugate gradient descent, quasi-Newton, Levenberg-Marquardt, etc., but the most employed one is the back-propagation algorithm. This algorithm uses the error values of the output layer (prediction) to adjust the weight of layer connections. Therefore, this algorithm provides a guarantee of minimum (local or global) convergence [38,48-50].

The main challenge of MLP is the choice of the most suitable architecture. The speed and the performance of the MLP learning are strongly affected by the number of layers and the number of hidden unities in each layer [38,48-50]. Figure 3 displays the influence of number of layers on the pattern recognition ability of neural network.

Figure 3.

Influence of the number of layers on the pattern recognition ability of MLP (adapted from Jain & Mao, 1996 [25]).

The increase in the number of layers in a MLP algorithm is proportional to the increase of complexity of the problem to be solved. The higher the number of hidden layers, the higher the complexity of the pattern recognition of the neural network.

2.2. Self-organizing map or Kohonen neural network

Self-organizing map (SOM), also called Kohonen neural network (KNN), is an unsupervised neural network designed to perform a non-linear mapping of a high-dimensionality data space transforming it in a low-dimensional space, usually a bidimensional space. The visualization of the output data is performed from the distance/proximity of neurons in the output 2D-layer. In other words, the SOM technique is employed to cluster and extrapolate the data set keeping the original topology. The SOM output neurons are only connected to its nearest neighbors. The neighborhood represents a similar pattern represented by an output neuron. In general, the neighborhood of an output neuron is defined as square or hexagonal and this means that each neuron has 4 or 6 nearest neighbors, respectively [51-53]. Figure 4 exemplifies the output layers of a SOM model using square and hexagonal neurons for a combinatorial design of purinergic receptor antagonists [54] and cannabinoid compounds [30], respectively.

Figure 4.

Example of output layers of SOM models using square and hexagonal neurons for the combinatorial design of (a) purinergic receptor antagonists [54] and (b) cannabinoid compounds [30], respectively.

The SOM technique could be considered a competitive neural network due to its learning algorithm. The competitive learning means that only the neuron in the output layer is selected if its weight is the most similar to the input pattern than the other input neurons. Finally, the learning rate for the neighborhood is scaled down proportional to the distance of the winner output neuron [51-53].

2.3. Bayesian regularized artificial neural networks

Different from the usual back-propagation learning algorithm, the Bayesian method considers all possible values of weights of a neural network weighted by the probability of each set of weights. This kind of neural network is called Bayesian regularized artificial neural (BRANN) networks because the probability of distribution of each neural network, which provides the weights, can be determined by Bayes’s theorem [55]. Therefore, the Bayesian method can estimate the number of effective parameters to predict an output data, practically independent from the ANN architecture. As well as the MLP technique, the choice of the network architecture is a very important step for the learning of BRANN. A complete review of the BRANN technique can be found in other studies [56-59].

2.4. Other important neural networks

Adaptative resonance theory (ART) neural networks [60,61] constitute other mathematical models designed to describe the biological brain behavior. One of the most important characteristic of this technique is the capacity of knowledge without disturbing or destroying the stored knowledge. A simple variation of this technique, the ART-2a model, has a simple learning algorithm and it is practically inexpensive compared to other ART models [60-63]. The ART-2a method consists in constructing a weight matrix that describes the centroid nature of a predicted class [62,63]. In the literature, there are several chemical studies that employ the ART-based neural networks [64-73].

The neural network known as radial basis function (RBF) [74] typically has the input layer, a hidden layer with a RBF as the activation function and the output layer. This network was developed to treat irregular topographic contours of geographical data [75-76] but due to its capacity of solving complex problems (non-linear specially), the RBF networks have been successfully employed to chemical problems. There are several studies comparing the robustness of prediction (prediction coefficients, r2, pattern recognition rates and errors) of RBF-based networks and other methods [77-80].

The Hopfield neural network [81-82] is a model that uses a binary n x n matrix (presented as n x n pixel image) as a weight matrix for n input signals. The activation function treats the activation signal only as 1 or -1. Besides, the algorithm treats black and white pixels as 0 and 1 binary digits, respectively, and there is a transformation of the matrix data to enlarge the interval from 0 – 1 to (-1) – (+1). The complete description of this technique can be found in reference [47]. In chemistry research, we can found some studies employing the Hopfield model to obtain molecular alignments [83], to calculate the intermolecular potential energy function from the second virial coefficient [84] and other purposes [85-86].


3. Applications

Following, we will present a brief description of some studies that apply ANN techniques as important tools to solve chemical problems.

3.1. Medicinal Chemistry and Pharmaceutical Research

The drug design research involves the use of several experimental and computational strategies with different purposes, such as biological affinity, pharmacokinetic and toxicological studies, as well as quantitative structure-activity relationship (QSAR) models [87-95]. Another important approach to design new potential drugs is virtual screening (VS), which can maximize the effectiveness of rational drug development employing computational assays to classify or filter a compound database as potent drug candidates [96-100]. Besides, various ANN methodologies have been largely applied to control the process of the pharmaceutical production [101-104].

Fanny et al. [105] constructed a SOM model to perform VS experiments and tested an external database of 160,000 compounds. The use of SOM methodology accelerated the similarity searches by using several pharmacophore descriptors. The best result indicated a map that retrieves 90% of relevant neighbors (output neurons) in the similarity search for virtual hits.

3.2. Theoretical and Computational Chemistry

In theoretical/computational chemistry, we can obtain some applications of ANN techniques such as the prediction of ionization potential [106], lipophilicity of chemicals [107, 108], chemical/physical/mechanical properties of polymer employing topological indices [109] and relative permittivity and oxygen diffusion of ceramic materials [110].

Stojković et al. [111] also constructed a quantitative structure-property relationship (QSPR) model to predict pKBH+ for 92 amines. To construct the regression model, the authors calculated some topological and quantum chemical descriptors. The counter-propagation neural network was employed as a modeling tool and the Kohonen self-organizing map was employed to graphically visualize the results. The authors could clearly explain how the input descriptors influenced the pKBH+ behavior, in special the presence of halogens atoms in the amines structure.

3.3. Analytical Chemistry

There are several studies in analytical chemistry employing ANN techniques with the aim to obtain multivariate calibration and analysis of spectroscopy data [112-117], as well as to model the HPLC retention behavior [118] and reaction kinetics [119].

Fatemi [120] constructed a QSPR model employing the ANN technique with back-propagation algorithm to predict the ozone tropospheric degradation rate constant of organic compounds. The data set was composed of 137 organic compounds divided into training, test and validation sets. The author also compared the ANN results with those obtained from the MLR method. The correlation coefficients obtained with ANN/MLR were 0.99/0.88, 0.96/0.86 and 0.96/0.74 for the training, test and validation sets, respectively. These results showed the best efficacy of the ANN methodology in this case.

3.4. Biochemistry

Neural networks have been largely employed in biochemistry and correlated research fields such as protein, DNA/RNA and molecular biology sciences [121-127].

Petritis et al. [128] employed a three layer neural network with back-propagation algorithm to predict the reverse-phase liquid chromatography retention time of peptides enzymatically digested from proteomes. In the training set, the authors used 7000 known peptides from D. radiodurans. The constructed ANN model was employed to predict a set with 5200 peptides from S. oneidensis. The used neural network generated some weights for the chromatographic retention time for each aminoacid in agreement to results obtained by other authors. The obtained ANN model could predict a peptide sequence containing 41 aminoacids with an error less than 0.03. Half of the test set was predicted with less than 3% of error and more than 95% of this set was predicted with an error around 10%. These results showed that the ANN methodology is a good tool to predict the peptide retention time from liquid chromatography.

Huang et al. [129] introduced a novel ANN approach combining aspects of QSAR and ANN and they called this approach of physics and chemistry-driven ANN (Phys-Chem ANN). This methodology has the parameters and coefficients clearly based on physicochemical insights. In this study, the authors employed the Phys-Chem ANN methodology to predict the stability of human lysozyme. The data set was composed by 50 types of mutated lysozymes (including the wild type) and the experimental property used in the modeling was the change in the unfolding Gibbs free energy (kJ-1 mol). This study resulted in significant coefficients of calibration and validation (r2=0.95 and q2=0.92, respectively). The proposed methodology provided good prediction of biological activity, as well as structural information and physical explanations to understand the stability of human lysozyme.

3.5. Food Research

ANNs have also been widely employed in food research. Some examples of application of ANNs in this area include vegetable oil studies [130-138], beers [139], wines [140], honeys [141-142] and water [143-144].

Bos et al. [145] employed several ANN techniques to predict the water percentage in cheese samples. The authors tested several different architecture of neurons (some functions were employed to simulate different learning behaviors) and analyzed the prediction errors to assess the ANN performance. The best result was obtained employing a radial basis function neural network.

Cimpoiu et al. [146] used the multi-layer perceptron with the back-propagation algorithm to model the antioxidant activity of some classes of tea such as black, express black and green teas. The authors obtained a correlation of 99.9% between experimental and predicted antioxidant activity. A classification of samples was also performed using an ANN technique with a radial basis layer followed by a competitive layer with a perfect match between real and predicted classes.


4. Conclusions

Artificial Neural Networks (ANNs) were originally developed to mimic the learning process of human brain and the knowledge storage functions. The basic unities of ANNs are called neurons and are designed to transform the input data as well as propagating the signal with the aim to perform a non-linear correlation between experimental and predicted data. As the human brain is not completely understood, there are several different architectures of artificial neural networks presenting different performances. The most common ANNs applied to chemistry are MLP, SOM, BRANN, ART, Hopfield and RBF neural networks. There are several studies in the literature that compare ANN approaches with other chemometric tools (e.g. MLR and PLS), and these studies have shown that ANNs have the best performance in many cases. Due to the robustness and efficacy of ANNs to solve complex problems, these methods have been widely employed in several research fields such as medicinal chemistry, pharmaceutical research, theoretical and computational chemistry, analytical chemistry, biochemistry, food research, etc. Therefore, ANN techniques can be considered valuable tools to understand the main mechanisms involved in chemical problems.


Techniques related to artificial neural networks (ANNs) have been increasingly used in chemical studies for data analysis in the last decades. Some areas of ANN applications involve pattern identification, modeling of relationships between structure and biological activity, classification of compound classes, identification of drug targets, prediction of several physicochemical properties and others. Actually, the main purpose of ANN techniques in chemical problems is to create models for complex input–output relationships based on learning from examples and, consequently, these models can be used in prediction studies. It is interesting to note that ANN methodologies have shown their power and robustness in the creation of useful models to help chemists in research projects in academy and industry. Nowadays, the evolution of computer science (software and hardware) has allowed the development of many computational methods used to understand and simulate the behavior of complex systems. In this way, the integration of technological and scientific innovation has helped the treatment of large databases of chemical compounds in order to identify possible patterns. However, people that can use computational techniques must be prepared to understand the limits of applicability of any computational method and to distinguish between those opportunities which are appropriate to apply ANN methodologies to solve chemical problems. The evolution of ANN theory has resulted in an increase in the number of successful applications. So, the main contribution of this book chapter will be briefly outline our view on the present scope and future advances of ANNs based on some applications from recent research projects with emphasis in the generation of predictive ANN models.


  1. 1. Teófilo R. F. Ferreira M. M. C. 2006 Quimiometria II: Planilhas Eletrônicas para Cálculos de Planejamento Experimental um Tutorial Quim. Nova 29 338 350
  2. 2. Lundstedt T. Seifert E. Abramo L. Theilin B. Nyström A. Pettersen J. Bergman R. 1998 Experimental design and optimization Chemom. Intell. Lab. 42 3 40
  3. 3. Kowalski B. R. J. 1957 Chemometrics: Views and Propositions J. Chem. Inf. Comp. Sci. 15 201 203
  4. 4. Wold S. 1976 Pattern recognition by means of disjoint principal component models Pattern Recognition 8 127 139
  5. 5. Vandeginste B. G. M. 1987 Chemometrics- General Introduction and Historical Development Top. Curr. Chem. 141 1 42
  6. 6. Wold S. Sjöström M. 1998 Chemometrics present and future success Chemom. Intell. Lab. 44 3 14
  7. 7. Ferreira M. M. C. Antunes A. M. Melgo M. S. Volpe P. L. O. 1999 Quimiometria I: calibração multivariada um tutorial Quím. Nova 22 724 731
  8. 8. Neto B. B. Scarminio I. S. Bruns R. E. 2006 Anos de Quimiometria no Brasil Quim. Nova 29 1401 1406
  9. 9. Hopke P. K. 2003 The evolution of chemometrics Anal. Chim. Acta 500 365 377
  10. 10. Wold S. Ruhe A. Wold H. Dunn W. 1984 The collinearity problem in linear regression: The partial least squares approach to generalized inverses SIAM J. Sci. Stat. Comput. 5 753 743
  11. 11. Wold S. Sjöströma M. Eriksson L. 2001 PLS-regression: a basic tool of chemometrics Chemom. Intell. Lab. 58 109 130
  12. 12. MATLAB r MathWorks Inc. 2011
  13. 13. RStudioTM 3 RStudio Inc. 2012
  14. 14. Statistica Data Analysis Software System 2011
  15. 15. Pirouette Infometrix Inc. 2001
  16. 16. HQSARTM Manual Release in Sybyl 7.3. Tripos Inc. 2007
  17. 17. Cramer R. D. Patterson D. E. Bunce J. D. 1988 Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins J. Am. Chem. Soc. 110 5959 5967
  18. 18. Cramer R. D. Patterson D. E. Bunce J. D. 1989 Recent advances in comparative molecular field analysis (CoMFA) Prog. Clin. Biol. Res. 291 161 165
  19. 19. Klebe G. Abraham U. Mietzner T. 1994 Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity J. Med. Chem. 37 4130 4146
  20. 20. Martins J. P. A. Barbosa E. G. Pasqualoto K. F. M. Ferreira M. M. C. 2009 LQTA-QSAR: A New 4D-QSAR Methodology J. Chem. Inf. Model. 49 1428 1436
  21. 21. Long J. R. Gregoriou V. G. Gemperline P. J. 1990 Spectroscopic calibration and quantitation using artificial neural networks Anal. Chem. 62 1791 1797
  22. 22. Cerqueira E. O. Andrade J. C. Poppi R. J. 2001 Redes neurais e suas aplicações em calibração multivariada Quím. Nova 24 864 873
  23. 23. Sigman M. E. Rives S. S. 1994 Prediction of Atomic Ionization Potentials I-III Using an Artificial Neural Network J. Chem. Inf. Comput. Sci. 34 617 620
  24. 24. Hsiao T. Lin C. Zeng M. Chiang H. K. 1998 The Implementation of Partial Least Squares with Artificial Neural Network Architecture Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 20 1341 1343
  25. 25. Jain A. K. Mao J. Mohiuddin K. M. 1996 Artificial Neural Networks: A Tutorial IEEE Computer 29 31 44
  26. 26. Borggaard C. Thodberg H. H. 1992 Optimal minimal neural interpretation of spectra Anal. Chem. 64 545 551
  27. 27. Zheng F. Zheng G. Deaciuc A. G. Zhan C. G. Dwoskin L. P. Crooks P. A. 2007 Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2 Bioorg. Med. Chem. 15 2975 2992
  28. 28. Louis B. Agrawal V. K. Khadikar P. V. 2010 Prediction of intrinsic solubility of generic drugs using mlr, ann and svm analyses Eur. J. Med. Chem. 45 4018 4025
  29. 29. Fatemi M. H. Heidari A. Ghorbanzade M. 2010 Prediction of aqueous solubility of drug-like compounds by using an artificial neural network and least-squares support vector machine Bull. Chem. Soc. Jpn. 83 1338 1345
  30. 30. Honório K. M. de Lima E. F. Quiles M. G. Romero R. A. F. Molfetta F. A. da Silva. A. B. F. 2010 Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds Chem. Biol. Drug. Des. 75 632 640
  31. 31. Qin Y. Deng H. Yan H. Zhong R. 2011 An accurate nonlinear QSAR model for the antitumor activities of chloroethylnitrosoureas using neural networks J. Mol. Graph. Model. 29 826 833
  32. 32. Himmelblau D. M. 2000 Applications of artificial neural networks in chemical engineering Korean J. Chem. Eng. 17 373 392
  33. 33. Marini F. Bucci R. Magri A. L. Magri A. D. 2008 Artificial neural networks in chemometrics: History examples and perspectives Microchem. J. 88 178 185
  34. 34. Mc Cutloch W. S. Pttts W. 1943 A logical calculus of the Ideas imminent in nervous activity Bull. Math. Biophys. 5 115 133
  35. 35. Pitts W. Mc Culloch W. S. 1947 How we know universals: the perception of auditory and visual forms Bull. Math. Biophys. 9 127 147
  36. 36. Hebb D. O. 1949 The Organization of Behavior New York Wiley
  37. 37. Zupan J. Gasteiger J. 1991 Neural networks: A new method for solving chemical problems or just a passing phase? Anal. Chim. Acta 248 1 30
  38. 38. Smits J. R. M. Melssen W. J. Buydens L. M. C. Kateman G. 1994 Using artificial neural networks for solving chemical problems. Part I. Multi-layer feed-forward networks Chemom. Intell. Lab. 22 165 189
  39. 39. Hopfield J. J. 1982 Neural networks and physical systems with emergent collective computational abilities Proc. Nat. Acad. Set. 79 2554 2558
  40. 40. Werbos P. 1974 Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences PhD thesis Harvard University Cambridge
  41. 41. Hoskins J. C. Himmelbau D. M. 1988 Artificial Neural Network Models of Knowledge Representation in Chemical Engineering Comput. Chem. Eng. 12 881 890
  42. 42. Qian N. Sejnowski T. J. 1988 Predicting the Secondary Structure of Globular Proteins Using Neural Network Models J. Mol. Biol. 202 865 884
  43. 43. Bohr H. Bohr J. Brunak S. Cotterill R. Lautrup B. Norskov L. Olsen O. Petersen S. 1988 Protein Secondary Structure and Homology by Neural Networks FEBS Lett. 241 223 228
  44. 44. Hassoun M. H. 2003 Fundamentals of Artificial Neural Networks. A Bradford Book
  45. 45. Zurada J. M. 1992 Introduction to Artificial Neural Systems Boston PWS Publishing Company
  46. 46. Zupan J. Gasteiger J. 1999 Neural Networks in Chemistry and Drug Design 2 ed. Wiley-VCH
  47. 47. Gasteiger J. Zupan J. 1993 Neural Networks in Chemistry Angew. Chem. Int. Edit. 32 503 527
  48. 48. Marini F. 2009 Artificial neural networks in foodstuff analyses: Trends and perspectives A review. Anal. Chim. Acta 635 121 131
  49. 49. Miller F. P. Vandome A. F. Mc Brewster J. 2011 Multilayer Perceptron Alphascript Publishing
  50. 50. Widrow B. Lehr M. A. 1990 30 years of Adaptive Neural Networks: Perceptron Madaline and Backpropagation Proc. IEEE 78 1415 1442
  51. 51. Kohonen T. 2001 Self Organizing Maps 3 ed. New York Springer
  52. 52. Zupan J. Noviča M. Ruisánchez I. 1997 Kohonen and counterpropagation artificial neural networks in analytical chemistry Chemom. Intell. Lab. 38 1 23
  53. 53. Smits J. R. M. Melssen W. J. Buydens L. M. C. Kateman G. 1994 Using artificial neural networks for solving chemical problems. Part II. Kohonen self-organising feature maps and Hopfield networks Chemom. Intell. Lab. 23 267 291
  54. 54. Schneider G. Nettekoven M. 2003 Ligand-based combinatorial design of selective purinergic receptor (a2a) antagonists using self-organizing maps J. Comb. Chem. 5 233
  55. 55. Bayes T. 1764 An Essay toward solving a problem in the doctrine of chances Philosophical Transactions of the Royal Society of London 53 370 418
  56. 56. Mackay D. J. C. 1995 Probable Networks and Plausible Predictions- a Review of Practical Bayesian Methods for Supervised Neural Networks Comput. Neural Sys. 6 469 505
  57. 57. Mackay D. J. C. 1992 Bayesian Interpolation Neural Comput. 4 415 447
  58. 58. Buntine W. L. Weigend A. S. 1991 Bayesian Back-Propagation Complex. Sys. 5 603 643
  59. 59. de Freitas J. F. G. 2003 Bayesian Methods for Neural Networks PhD thesis University Engineering Dept Cambridge
  60. 60. Grossberg S. 1976 Adaptive pattern classification and universal recoding I. Parallel development and coding of neural feature detectors Biol. Cybern. 23 121 134
  61. 61. Grossberg S. 1976 Adaptive pattern classification and universal recoding II. Feedback expectation olfaction and illusions Biol. Cybern. 23 187 203
  62. 62. Carpenter G. A. Grossberg S. Rosen D. B. 1991 ART-2a-an adaptive resonance algorithm for rapid category learning and recognition Neural Networks 4 493 504
  63. 63. Wienke D. Buydens L. 1995 Adaptive resonance theory based neural networks- the’ART’ of real-time pattern recognition in chemical process monitoring? TrAC Trend. Anal. Chem. 14 398 406
  64. 64. Lin C. C. Wang H. P. 1993 Classification of autoregressive spectral estimated signal patterns using an adaptive resonance theory neural network Comput. Ind. 22 143 157
  65. 65. Whiteley J. R. Davis J. F. 1994 A similarity-based approach to interpretation of sensor data using adaptive resonance theory Comput. Chem. Eng. 18 637 661
  66. 66. Whiteley J. R. Davis J. F. 1993 Qualitative interpretation of sensor patterns IEEE Expert 4 54 63
  67. 67. Wienke D. Kateman G. 1994 Adaptive resonance theory based artificial neural networks for treatment of open-category problems in chemical pattern recognition- application to UV-Vis and IR spectroscopy Chemom. Intell. Lab. 23 309 329
  68. 68. Wienke D. Xie Y. Hopke P. K. 1994 An adaptive resonance theory based artificial neural network (ART-2a) for rapid identification of airborne particle shapes from their scanning electron microscopy images Intell. Lab. 26 367 387
  69. 69. Xie Y. Hopke P. K. Wienke D. 1994 Airborne particle classification with a combination of chemical composition and shape index utilizing an adaptive resonance artificial neural network Environ. Sci. Technol. 28 1921 1928
  70. 70. Wienke D. van den Broek. W. Melssen W. Buydens L. Feldhoff R. Huth-Fehre T. Kantimm T. Quick L. Winter F. Cammann K. 1995 Comparison of an adaptive resonance theory based neural network (ART-2a) against other classifiers for rapid sorting of post consumer plastics by remote near-infrared spectroscopic sensing using an InGaAs diode array Anal. Chim. Acta 317 1 16
  71. 71. Domine D. Devillers J. Wienke D. Buydens L. 1997 ART 2-A for Optimal Test Series Design in QSAR J. Chem. Inf. Comput. Sci. 37 10 17
  72. 72. Wienke D. Buydens L. 1996 Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP). Part 1: Theory and network properties Chemom. Intell. Lab. 32 151 164
  73. 73. Wienke D. van den Broek. W. Buydens L. Huth-Fehre T. Feldhoff R. Kantimm T. Cammann K. 1996 Adaptive resonance theory based neural network for supervised chemical pattern recognition (FuzzyARTMAP). Part 2: Classification of post-consumer plastics by remote NIR spectroscopy using an InGaAs diode array Chemom. Intell. Lab. 32 165 176
  74. 74. Buhmann M. D. 2003 Radial Basis Functions: Theory and Implementations Cambridge University
  75. 75. Lingireddy S. Ormsbee L. E. 1998 Neural Networks in Optimal Calibration of Water Distribution Systems. In: Flood I, Kartam N. (eds.) Artificial Neural Networks for Civil Engineers: Advanced Features and Applications Amer. Society of Civil Engineers 53 76
  76. 76. Shahsavand A. Ahmadpour A. 2005 Application of Optimal Rbf Neural Networks for Optimization and Characterization of Porous Materials Comput. Chem. Eng. 29 2134 2143
  77. 77. Regis R. G. Shoemaker C. 2005 A Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions J. Global. Optim. 31 153 171
  78. 78. Han H. Chen Q. Qiao J. 2011 An efficient self-organizing RBF neural network for water quality prediction Neural Networks 24 717 725
  79. 79. Fidêncio P. H. Poppi R. J. Andrade J. C. Abreu M. F. 2008 Use of Radial Basis Function Networks and Near-Infrared Spectroscopy for the Determination of Total Nitrogen Content in Soils from Sao Paulo State Anal. Sci. 24 945 948
  80. 80. Yao X. Liu M. Zhang X. Zhang R. Hu Z. Fan B. 2002 Radial Basis Function Neural Networks Based QSPR for the Prediction of log P Chinese J. Chem. 20 722 730
  81. 81. Hopfield J. J. 1982 Neural networks and physical systems with emergent collective computational abilities Proc. Natl. Acad. Sci. USA 79 2554 2558
  82. 82. Hopfield J. J. 1984 Neurons with graded response have collective computational properties like those of two-state neurons Proc. Natl. Acad. Sci. USA 81 3088 3092
  83. 83. Arakawa M. Hasegawa K. Funatsu K. 2003 Application of the Novel Molecular Alignment Method Using the Hopfield Neural Network to 3D-QSAR J. Chem. Inf. Comput. Sci. 43 1396 1402
  84. 84. Braga J. P. Almeida M. B. Braga A. P. Belchior J. C. 2000 Hopfield neural network model for calculating the potential energy function from second virial data Chem. Phys. 260 347 352
  85. 85. Hjelmfelt A. Ross J. 1992 Chemical implementation and thermodynamics of collective neural networks PNAS 89 388 391
  86. 86. Hjelmfelt A. Schneider F. W. Ross J. 1993 Pattern Recognition in Coupled Chemical Kinetic Systems Science 260 335 337
  87. 87. Vracko M. 2005 Kohonen Artificial Neural Network and Counter Propagation Neural Network in Molecular Structure-Toxicity Studies Curr. Comput-Aid. Drug 1 73 78
  88. 88. Guha R. Serra J. R. Jurs P. C. 2004 Generation of QSAR sets with a self-organizing map J. Mol. Graph. Model. 23 1 14
  89. 89. Hoshi K. Kawakami J. Kumagai M. Kasahara S. Nishimura N. Nakamura H. Sato K. 2005 An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks Chem. Pharm. Bull. 53 1570 1574
  90. 90. Nandi S. Vracko M. Bagchi M. C. 2007 Anticancer activity of selected phenolic compounds: QSAR studies using ridge regression and neural networks Chem. Biol. Drug Des. 70 424 436
  91. 91. Xiao Y. D. Clauset A. Harris R. Bayram E. Santago P. JD Schmitt 2005 Supervised self-organizing maps in drug discovery. 1. Robust behavior with overdetermined data sets J. Chem. Inf. Model. 45 1749 1758
  92. 92. Molfetta F. A. Angelotti W. F. D. Romero R. A. F. Montanari C. A. da Silva. A. B. F. 2008 A neural networks study of quinone compounds with trypanocidal activity J. Mol. Model. 14 975 985
  93. 93. Zheng F. Zheng G. Deaciuc A. G. Zhan C. G. Dwoskin L. P. Crooks P. A. 2007 Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2. Bioorg Med. Chem. 15 2975 2992
  94. 94. Caballero J. Fernandez M. Gonzalez-Nilo F. D. 2008 Structural requirements of pyrido[23-d]pyrimidin-7-one as CDK4/D inhibitors: 2D autocorrelation CoMFA and CoMSIA analyses Bioorg. Med. Chem. 16 6103 6115
  95. 95. Schneider G. Coassolo P. Lavé T. 1999 Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques J. Med. Chem. 42 5072 5076
  96. 96. Hu L. Chen G. Chau R. M. W. 2006 A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors J. Mol. Graph. Model. 24 244 253
  97. 97. Afantitis A. Melagraki G. Koutentis P. A. Sarimveis H. Kollias G. 2011 Ligand- based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks Eur. J. Med. Chem. 46 497 508
  98. 98. Noeske T. Trifanova D. Kauss V. Renner S. Parsons C. G. Schneider G. Weil T. 2009 Synergism of virtual screening and medicinal chemistry: Identification and optimization of allosteric antagonists of metabotropic glutamate receptor 1. Bioorg. Med. Chem. 17 5708 5715
  99. 99. Karpov P. V. Osolodkin D. I. Baskin I. I. Palyulin V. A. Zefirov N. S. 2011 One-class classification as a novel method of ligand-based virtual screening: The case of glycogen synthase kinase 3β inhibitors Bioorg. Med. Chem. Lett. 21 6728 6731
  100. 100. Molnar L. Keseru G. M. 2002 A neural network based virtual screening of cytochrome p450a4 inhibitors Bioorg. Med. Chem. Lett. 12 419 421
  101. 101. Di Massimo C. Montague G. A. MJ Willis Tham. M. T. Morris A. J. 1992 Towards improved penicillin fermentation via artificialneuralnetworks Comput. Chem. Eng. 16 283 291
  102. 102. Palancar M. C. Aragón J. M. Torrecilla J. S. 1998 pH-Control System Based on Artificial Neural Networks Ind. Eng. Chem. Res. 37 2729 2740
  103. 103. Takayama K. Fujikawa M. Nagai T. 1999 Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations Pharm. Res. 16 1 6
  104. 104. Takayama K. Morva A. Fujikawa M. Hattori Y. Obata Y. Nagai T. 2000 Formula optimization of theophylline controlled-release tablet based on artificial neural networks J. Control. Release 68 175 186
  105. 105. Fanny B. Gilles M. Natalia K. Alexandre V. Dragos H. Using Self-Organizing Maps to Accelerate Similarity Search Bioorg. Med. Chem. In Press http://dxdoiorg/101016/jbmc201204024
  106. 106. Sigman M. E. Rives S. S. 1994 Prediction of Atomic Ionization Potentials I-III Using an Artificial Neural Network J. Chem. Inf. Comput. Sci. 34 617 620
  107. 107. Tetko I. V. Tanchuk V. Y. 2002 Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program J. Chem. Inf. Comput. Sci. 42 1136 1145
  108. 108. Tetko I. V. Tanchuk V. Y. Villa A. E. P. 2001 Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices J. Chem. Inf. Comput. Sci. 41 1407 1421
  109. 109. Sumpter B. G. Noid D. W. 1994 Neural networks and graph theory as computational tools for predicting polymer properties Macromol. Theor. Simul. 3 363 378
  110. 110. Scotta D. J. Coveneya P. V. Kilnerb J. A. Rossinyb J. C. H. Alford N. M. N. 2007 Prediction of the functional properties of ceramic materials from composition using artificialneuralnetworks J. Eur. Ceram. Soc. 27 4425 4435
  111. 111. Stojković G. Novič M. Kuzmanovski I. 2010 Counter-propagation artificial neural networks as a tool for prediction of pKBH+ for series of amides Chemom. Intell. Lab. 102 123 129
  112. 112. Næs T. Kvaal K. Isaksson T. Miller C. 1993 Artificial neural networks in multivariate calibration J. Near. Infrared Spectrosc. 1 1 11
  113. 113. Munk M. E. Madison M. S. Robb E. W. 1991 Neural-network models for infrared-spectrum interpretation Mikrochim. Acta 2 505 524
  114. 114. Meyer M. Weigelt T. 1992 Interpretation of infrared spectra by artificial neural networks Anal. Chim. Acta 265 183 190
  115. 115. Smits J. R. M. Schoenmakers P. Stehmann A. Sijstermans F. Chemom Kateman G. 1993 Interpretation of infrared spectra with modular neural-network systems Intell. Lab. 18 27 39
  116. 116. Goodacre R. Neal M. J. Kell D. B. 1994 Rapid and Quantitative Analysis of the Pyrolysis Mass Spectra of Complex Binary and Tertiary Mixtures Using Multivariate Calibration and Artificial Neural Networks Anal. Chem. 66 1070 1085
  117. 117. Cirovic D. 1997 Feed-forward artificial neural networks: applications to spectroscopy TrAC Trend. Anal. Chem. 16 148 155
  118. 118. Zhao R. H. Yue B. F. Ni J. Y. Zhou H. F. Zhang Y. K. 1999 Application of an artificial neural network in chromatography-retention behavior prediction and pattern recognition Chemom. Intell. Lab. 45 163 170
  119. 119. Blanco M. Coello J. Iturriaga H. Maspoch S. Redon M. 1995 Artificial Neural Networks for Multicomponent Kinetic Determinations Anal. Chem. 67 4477 4483
  120. 120. Fatemi M. H. 2006 Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks Anal. Chim. Acta 556 355 363
  121. 121. Diederichs K. Freigang J. Umhau S. Zeth K. Breed J. 1998 Prediction by a neural network of outer membrane {beta}-strand protein topology Protein Sci. 7 2413 2420
  122. 122. Meiler J. 2003 PROSHIFT: Protein chemical shift prediction using artificial neural networks J. Biomol. NMR 26 25 37
  123. 123. Lohmann R. Schneider G. Behrens D. Wrede P. A. 1994 Neural network model for the prediction of membrane-spanning amino acid sequences Protein Sci. 3 1597 1601
  124. 124. Dombi G. W. Lawrence J. 1994 Analysis of protein transmembrane helical regions by a neural network Protein Sci. 3 557 566
  125. 125. Wang S. Q. Yang J. Chou K. C. 2006 Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition J. Theor. Biol. 242 941 946
  126. 126. Ma L. Cheng C. Liu X. Zhao Y. Wang A. Herdewijn P. 2004 A neural network for predicting the stability of RNA/DNA hybrid duplexes. Chemom Intell. Lab. 70 123 128
  127. 127. Ferran E. A. Pflugfelaer B. Ferrara P. 1994 Self-organized neural maps of human protein sequences Protein Sci. 3 507 521
  128. 128. Petritis K. Kangas L. J. Ferguson P. L. Anderson G. A. Paša-Tolić L. Lipton M. S. Auberry K. J. Strittmatter E. F. Shen Y. Zhao R. Smith R. D. 2003 Use of Artificial Neural Networks for the Accurate Prediction of Peptide Liquid Chromatography Elution Times in Proteome Analyses Anal. Chem. 75 1039 1048
  129. 129. Huang R. Du Q. Wei Y. Pang Z. Wei H. Chou K. 2009 Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design J. Theor. Biol. 256 428 435
  130. 130. Martin Y. G. Oliveros M. C. C. Pavon J. L. P. Pinto C. G. Cordero B. M. 2001 Electronic nose based on metal oxide semiconductor sensors and pattern recognition techniques: characterisation of vegetable oils Anal. Chim. Acta 449 69 80
  131. 131. Brodnjak-Voncina D. Kodba Z. C. Novic M. 2005 Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids Chemom. Intell. Lab. 75 31 43
  132. 132. Zhang G. W. Ni Y. N. Churchill J. Kokot S. 2006 Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics Talanta 70 293 300
  133. 133. Goodacre R. Kell D. B. Bianchi G. 1993 Rapid assessment of the adulteration of virgin olive oils by other seed oils using pyrolysis mass spectrometry and artificial neural networks J. Sci. Food Agr. 63 297 307
  134. 134. Bianchi G. Giansante L. Shaw A. Kell D. B. 2001 Chemometric criteria for the characterisation of Italian DOP olive oils from their metabolic profiles Eur. J. Lipid. Sci. Tech. 103 141 150
  135. 135. Bucci R. Magri A. D. Magri A. L. Marini D. Marini F. 2002 Chemical Authentication of Extra Virgin Olive Oil Varieties by Supervised Chemometric Procedures J. Agric. Food Chem. 50 413 418
  136. 136. Marini F. Balestrieri F. Bucci R. Magri A. D. Magri A. L. Marini D. 2004 Supervised pattern recognition to authenticate Italian extra virgin olive oil varieties Chemom. Intell. Lab. 73 85 93
  137. 137. Marini F. Balestrieri F. Bucci R. Magri A. L. Marini D. 2003 Supervised pattern recognition to discriminate the geographical origin of rice bran oils: a first study Microch. J. 74 239 248
  138. 138. Marini F. Magri A. L. Marini D. Balestrieri F. 2003 Characterization of the lipid fraction of Niger seeds (Guizotia abyssinica cass) from different regions of Ethiopia and India and chemometric authentication of their geographical origin Eur. J. Lipid. Sci. Tech. 105 697 704
  139. 139. Alexander P. W. Di Benedetto L. T. Hibbert D. B. 1998 A field-portable gas analyzer with an array of six semiconductor sensors. Part 2: Identification of beer samples using artificial neural networks Field. Anal. Chem. Tech. 2 145 153
  140. 140. Penza M. Cassano G. 2004 Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks Food Chem. 86 283 296
  141. 141. MJ Latorre Pena. R. Garcia S. Herrero C. 2000 Authentication of Galician (NW Spain) honeys by multivariate techniques based on metal content data Analyst. 125 307 312
  142. 142. Cordella C. B. Y. Militao J. S. L. T. Clement M. C. 2003 Cabrol-Bass D Honey Characterization and Adulteration Detection by Pattern Recognition Applied on HPAEC-PAD Profiles. 1. Honey Floral Species Characterization J. Agric. Food Chem. 51 3234 3242
  143. 143. Brodnjak-Voncina D. Dobcnik D. Novic M. Zupan J. 2002 Chemometrics characterisation of the quality of river water Anal. Chim. Acta 462 87 100
  144. 144. Voncina E. Brodnjak-Voncina D. Sovic N. Novic M. 2007 Chemometric characterisation of the Quality of Ground Waters from Different wells in Slovenia Acta Chim. Slov. 54 119 125
  145. 145. Bos A. Bos M. van der Linden W. E. 1992 Artificial neural networks as a tool for soft-modelling in quantitative analytical chemistry: the prediction of the water content of cheese Anal. Chim. Acta 256 133 144
  146. 146. Cimpoiu C. Cristea V. Hosu A. Sandru M. Seserman L. 2011 Antioxidant activity prediction and classification of some teas using artificial neural networks Food Chem. 127 1323 1328

Written By

Vinícius Gonçalves Maltarollo, Káthia Maria Honório and Albérico Borges Ferreira da Silva

Submitted: May 31st, 2012 Published: January 16th, 2013