The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility.

Predicting the equilibrium solubility of organic, crystalline materials at all relevant temperatures is crucial to the digital design of manufacturing unit operations in the chemical industries. The work reported in our current publication builds upon the limited number of recently published quantitative structure-property relationship studies which modelled the temperature dependence of aqueous solubility. One set of models was built to directly predict temperature dependent solubility, including for materials with no solubility data at any temperature. We propose that a modified cross-validation protocol is required to evaluate these models. Another set of models was built to predict the related enthalpy of solution term, which can be used to estimate solubility at one temperature based upon solubility data for the same material at another temperature. We investigated whether various kinds of solid state descriptors improved the models obtained with a variety of molecular descriptor combinations: lattice energies or 3D descriptors calculated from crystal structures or melting point data. We found that none of these greatly improved the best direct predictions of temperature dependent solubility or the related enthalpy of solution endpoint. This finding is surprising because the importance of the solid state contribution to both endpoints is clear. We suggest our findings may, in part, reflect limitations in the descriptors calculated from crystal structures and, more generally, the limited availability of polymorph specific data. We present curated temperature dependent solubility and enthalpy of solution datasets, integrated with molecular and crystal structures, for future investigations

A new application of PC-ANN in spectrophotometric determination of acidity constants of PAR

The acidity constants of the PAR were determined by Principal Component Analysis Artificial Neural Networks, using simulated and experimental spectral data. Triprotic acid mass balance equations and corresponding spectral profiles generated by a Gaussian model were used to simulate all required absorbance-pH data. A constant noise with zero mean and different standard deviations (1-3% of the maximum absorbance values) was superimposed on the generated simulated spectra. A triangular experimental design was used to select and produce the different simulated acidity constants. The effects of white noise at different levels were also studied to check the prediction ability of the model. A fully experimental data set, photometric titration data of PAR at pH=1.50-13.00 range was used as a test set. The obtained acidity constants are in a good agreement with previously reported values using DATAN software