Solubility Prediction in Novel Chemical Space: Combining Statistics and DFT Calculations

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An improved protocol for in silico solubility prediction using statistical tools such assingle/multivariate linear regression (S/MLR) and principle component analysis (PCA) is proposed. Therequired descriptors for statistical analysis will be generated by DFT calculations, in contrast to the commonClogP fragments approach, to encompass both global and local structural properties of the solute, solutesolventand solute-solute lattice interactions. Principle component analysis (PCA) and chemical spacemapping with these descriptors will enable selective experimental solubility measurements to expand thechemical space coverage of the method. A training set of data, including difficult to predict salts and ioniccompounds, will be used to develop the protocol, before validation and final evaluation with a test set ofcompounds. Upon completing these successfully, the project will switch focus to the expansion of reliablesolubility prediction into novel chemical space.