Neural network modeling. Dissociation of acetic and benzoic acids in aqueous-organic solvents

Abstract

Predictive correlation-regression and neural network models for description of the properties of aqueous-organic solvents on the strength of acetic and benzoic acids have been developed. Significant descriptors affecting the dissociation equilibrium of the acids have been found. The features of the solvation (reflected in the electrostatic, cohesion, and electron accepting interactions) on the acids strength have been disclosed. Prediction of the dissociation constants of acetic and benzoic acids has been successfully performed using a three-layer perceptron. Prospects of the neural network modeling for prediction of organic acids strength in aqueous-organic media have been demonstrated. A neural network classifier of acetic and benzoic acids strength based on the aqueous-alcoholic solvents descriptors has been developed and trained.