QSRR modelling of small pharmaceutical compounds in Liquid Chromatography

QSRR models were developed for retention prediction of small molecules using data at five pH conditions (2.0, 3.5, 5.0, 6.5, and 8.0) and for two gradient times of 20 and 60 minutes. Molecular descriptors were derived from the chemical structures of the compounds using SMILES and proportions of each form of the compound of interest at the corresponding pH. MLR, Boruta, Lasso,SR were used as feature selection methods. One unique feature set was generted based on mutually inclusive features among base feature selection algorithms. 60 mixed qsrr models were developed using algorithms - LR,SVR,RF,GBM and stacked ensemble learning. The best model was then used to predict the retention times of the external test compound at each pH condition. We propose that our strategy involving stacked ensemble learning is very unique and effective method for retention prediction of small molecules.Chemical Information Mining in a Complex Worl