Solubility, neural network

Recently, several QSPR solubility prediction models based on a fairly large and diverse data set were generated. Huuskonen developed the models using MLRA and back-propagation neural networks (BPG) on a data set of 1297 diverse compoimds [22]. The compounds were described by 24 atom-type E-state indices and six other topological indices. For the 413 compoimds in the test set, MLRA gave = 0.88 and s = 0.71 and neural network provided... 

Figure 10.1-3. Predicted versus experimental solubility values of 552 compounds in the test set by a back-propagation neural network with 18 topological descriptors.

In this approach, connectivity indices were used as the principle descriptor of the topology of the repeat unit of a polymer. The connectivity indices of various polymers were first correlated directly with the experimental data for six different physical properties. The six properties were Van der Waals volume (Vw), molar volume (V), heat capacity (Cp), solubility parameter (5), glass transition temperature Tfj, and cohesive energies ( coh) for the 45 different polymers. Available data were used to establish the dependence of these properties on the topological indices. All the experimental data for these properties were trained simultaneously in the proposed neural network model in order to develop an overall cause-effect relationship for all six properties. 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

There are many different methods for selecting those descriptors of a molecule that capture the information that somehow encodes the compounds solubility. Currently, the most often used are multiple linear regression (MLR), partial least squares (PLS) or neural networks (NN). The former two methods provide a simple linear relationship between several independent descriptors and the solubility, as given in Eq. (14). This equation yields the independent contribution, hi, of each descriptor, Di, to the solubility ... 

Huuskonen, J., Salo, M., Taskinen, J., Aqueous solubility prediction of drugs based on molecular topology and neural network modeling, J. 

From a modeling standpoint, the prediction of a molecule s solubility is a very difficult task because of the issues listed above [13-15]. The problem of predicting solubility has been attacked with reasonable success with complex neural network models. While not interpretable, neural networks can function as an in silico assay. Other techniques which are more interpretable have also been applied to the problem. 

Kiss IZ, Mandi G, Beck MT (2000) Artificial neural network approach to predict the solubility of C60 in various solvents. J. Phys. Chem. Sect A 104 8081-8088. 

In addition to methods that rely on experimental inputs, there are prediction tools that are based on structure alone. A number of different techniques have been used to correlate a variety of structure-derived descriptors with observed solubilities, including linear regression [20-25] and neural networks [26-36]. Interestingly, despite... 

Associative neural networks (ASNN) Aqueous solubility, octanol-water partition (logP, logD) Quantitative Error No [13-15]... 

Neural network/ genetic algorithm feature selection Aqueous solubility Quantitative No [8]... 

Bodor, et al. [42] compare the use of artificial neural networks with regression analysis techniques for the development of predictive solubility models. They report that the performance of the neural network model is superior to the regression-based model. Their study is based on a training set of 331 compounds. The model requires a diverse set of molecular descriptors to account for the structural variety in the training compounds. 

Jouyban et al. (2004) applied ANN to calculate the solubility of drugs in water-cosolvent mixtures, using 35 experimental datasets. The networks employed were feedforward back-propagation errors with one hidden layer. The topology of neural network was optimized in a 6-5-1 architecture. All data points in each set were used to train the ANN and the solubilities were back-calculated employing the trained networks. The difference between calculated solubilities and experimental... 

Although various computational approaches for the prediction of intestinal drug permeability and solubility have been reported [219], recent computer-based absorption models utilize a large number of topological, electronic, and geometric descriptors in an effort to take both aqueous drug solubility and permeability into account. Thus, descriptors of partitioned total surface areas [168], Abraham molecular descriptors [220,221], and a variety of structural descriptors in combination with neural networks [222] have been shown to be determinants of oral drug absorption. 

Duffythe group contribution approach of Klopman and the neural network model of Huuskonen. " The PCCHEM program used at the US Environmental Protection Agency (EPA) incorporates three different equations. All of them are similar to GSE but have different coefficients to predict aqueous solubility depending on the range of log P values. " Meylan and Howard used a database of 817 (RMSE = 0.62) compounds to derive a heuristic equation ... 

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