During the last two decades, human fungal infections have increased among immune compromised individuals (1). Candida albicans (C. albicans) is the major agent of candidosis in humans (2) which is the commonest invasive fungal infection in patients with malignant haematological disease and in bone marrow transplant recipients (3). One common cause of mortality among hospitalized patients is nosocomial infection due to opportunistic fungal pathogens (4). The development of azole-based antifungal drugs has revolutionized the treatment of many fungal infections, but therapy may still necessitate application of the highly toxic drug amphotericin B or a combination of drugs. Due to rapid emergence of resistance in fungal pathogens to the conventional drugs, discovery of new potent antifungal compounds is necessary. Plant extracts containing coumarin derivatives demonstrate antifungal activity (5) and some synthetic coumarin derivatives are also active against the yeast C. albicans (6). Coumarin is a benzopyrone and a naturally occurring constituent of many plants and essential oils, including tonka beans, sweet clover, woodru, oil of cassia and lavender (7). The presence of phenolic, hydroxy and carboxylic acid groups on the coumarin nucleus has been considered necessary for antimicrobial activity (8). The coumarins are extremely variable in structure and due to the various types of substitutions in the basic structural form their biological activity is influenced (9). As a result, a lot of biological parameters should be evaluated to increase our understanding of the mechanisms by which these coumarins act and a careful structure-property/activity-relationship study of coumarins should be conducted.

The so called "Cheminformatics" was introduced to the common use. It is often described as part of the analytical chemistry that by making use of mathematics, probability theory, mathematical statistics, as well as the decision-making theory and computer techniques, has been applied to a diverse range of problems in the field of chemistry (10). By combining together the elements of informatics and chemical analysis, cheminformatics appeared to be particularly useful in the professional work of pharmacists. It is concerned with the search for new chemical compounds as potential drugs, clinical analysis of these compounds, optimization of drug formulation, evaluation of its quality as well as leading to recognition of complicated processes in which the drug substances are involved in a human organism (11).

Among the multivariate analyses used in the cheminformatics, the principal component analysis (PCA), cluster analysis (CA) and artificial neural networks (ANNs) have been the most widely used methods (12). Their valuable features are that they can present the correct interpretation of the measured data and obtain the maximum useful information from them (13). A feed-forward Multi-layer Perceptron (MLP) neural network is the most commonly used paradigm in medicinal chemistry. They usually consist of an input layer, one output layer and one or two hidden or middle layer(s). All units in one layer are connected to all the units in the next layers (14). The signals flow from the first input layer forward through hidden nodes, where a weighed sum of inputs is computed and passed through activation function and the result is finally presented to the output layer. This process is called “feed-forward” (15).

A proper weight setting is not known beforehand and hence, initially, the weights are given a random value. The process of updating the weights to a correct set of values is called “Training or Learning”, which is mostly achieved by means of Backpropagation (BP) algorithm (16). The BP is a generalization of the least mean squared algorithm that modifies network weight to minimize the mean squared error between the desired and actual outputs of the network. The BP uses supervised learning in which the network is trained using data for which inputs as well as desired outputs are known (17).

The application of ANN's in solving different problems in pharmacy is receiving growing attention when it comes to data analysis problems (18). It is mainly because they are applicable in every situation in which a relationship between predictor variables (inputs) and predicted variables (output) exists, even when that relationship is very complex and not easy to express in the usual terms of correlation or differences between groups. Therefore, anywhere that there are problems of prediction, classification or control, neural networks prove to be helpful (19). Accordingly, in this study, neural computing is used for building an efficient model in order to evaluate the relationship between physicochemical properties and bioactivity of antifungal coumarins.

The results of this paper are based on investigation and analysis of collected or calculated data of several coumarin structural descriptors. The artificial neural network system was performed to build a powerful model for prediction of lead and template antifungal coumarins. Table 2 shows results of the various architectures of the neural network system. The numbers of hidden layer nodes were varied according to different node numbers and layers. One of the best architectures, considering the correlation behavior and output cycles of calculation was 11-8-4-1. The importance of an input descriptor is determined by the sum of the absolute values of the weights of all the outgoing architecture connections from the input node to the next layer. Some factors, such as surface tension, percent of oxygen, index of refraction, and percentage H have appeared among the most important factors. The least important descriptor was determined as the density. A range of predicted activity varied from 125.6796 to 3774.3753. The correlation coefficients between the experimental and the predicted MIC value pertaining to all the coumarins was 0.984 (Figure 1).

Compounds 67, 15, and 5 corresponded to the highest error that was generated during the training cycles. Y-Randomization result showed that the classification accuracy for randomized data sets was significantly lower than for the original data sets (data not shown) and hence we concluded that there is no evidence of over-fitting in our models. Cross validation is done by leave-some-out (some= 4) validating method. Validation showed that average of absolute errors was 0.379.

The artificial neural networks (ANNs) have become an important modeling technique in numerous areas of chemistry and pharmacy (30). The mathematical adaptability of ANN commends them as a powerful tool for pattern classification and building predictive models. A particular advantage of ANNs is their inherent ability to incorporate nonlinear dependencies between the dependent and independent variables without using an explicit mathematical function.

This study presents an approach to correlate the antifungal activity score data for a data set of drug-like molecules with the structural descriptors. In this study a nonlinear modeling technique of artificial neural network (ANN) with back propagation learning algorithm and sigmoid activation function was used. In this work, a MLP network (29) was developed and used to obtain a nonlinear SAR model. Topologically, it consisted of input, hidden, and output layers of neurons or units connected by weights. Each input layer node corresponded to a single independent variable (physicochemical descriptor) with the exception of the bias node. Similarly, each output layer node corresponded to a different dependent variable (property under investigation). In this study, all descriptors were derived solely from molecular structures which did not require experimental data or expensive theoretical calculations (to be obtained).

The ANN model was trained only on the training set since the validation set was used to monitor the external prediction error and thus to avoid overtraining. Among the 11 architectures constructed, the best ANN architecture we found was 11–8–4–1. That is, in the first layer eleven inputs comprised of eleven input descriptors, hidden layer comprised of seven neurons, and the last output layer comprised of one neuron for the property modeled. The statistical criteria obtained for the ANN model are shown in Table 2.

As it can be seen from this table the error for the training set is quite low. In addition, the errors for the validation set are also low showing the good prediction ability. The range of observed and predicted data criterion is very close to each other, that is, the overall prediction is close to experimental. Also, from these result we can conclude that the ANN model satisfactorily predicts the classification nature of the experimental data. Here, we should take into account that a large number of molecular descriptors are usually used in SAR methods. The specific biological action of drugs is frequently described by hydrophobic, electronic, steric and physicochemical properties. Physicochemical properties characterize the pharmacodynamic properties in the ligand– receptor interaction. They define the ability of the drug to join to the receptor.

The results of this ANN-based study indicate that surface tension is one of the most important factors in coumarin bioactivity. Surface tension of the molecule causes it to creep around the membrane, leading to formation of a layer of loaded molecules at the cell membrane quickly (31). This finding could describe how the LogP is the main sensitivity descriptor of the trained network. Sensitivity analysis is a measure of how the outputs change when the inputs are changed. Result of this paper could help to predict bioactivity of new coumarins.