Prediction of solubility of aliphatic alcohols using the restricted components of autocorrelation method (RCAM).

Structure-water solubility modeling of aliphatic alcohols was performed using the multifunctional autocorrelation method. The molecule is represented by using a set of parameters describing global molecules, and others that take the structural environment of the edge O-C into account. Multiple linear regression (MLR) and multilayer feed-forward artificial neural network architectures are utilized to construct linear and nonlinear QSPR models, respectively. The optimal QSPR model was developed based on a 4-4-1 neural network architecture. The efficiency of the approach is demonstrated through the predictive ability of the ANN and MLR models by the leave-20%-out (L20%O) cross-validation method, demonstrating that the neural model is more reliable than that obtained using MLR. The root mean square errors in the solubility prediction (ln SOL) for the calibration and predictive models were 0.13 and 0.18 respectively. On the other hand, we tested four activation functions: the hyperbolic tangent, sigmoid function or Gaussian functions for the hidden layer and a linear, sigmoid, hyperbolic tangent or Gaussian function for the output layer. The influence and the contribution of each type of descriptor in the model is examined. After omission of a set of descriptors, we calculate the error for the solubility and classify them into discrete categories. The standard error and the percentage of the prediction in the precision interval considered have been estimated. The results imply that the solubility of aliphatic alcohols is dominated by the shape and branching of the molecule. The hydrogen-bonding interactions caused by the C-OH group seem to be a less important factor influencing the solubility. The model was compared with other models; especially that using weighted path numbers, which is considered to be the most accurate QSPR model for predicting the water solubility of aliphatic alcohols.