Toward robust QSPR models: Synergistic utilization of robust regression and variable elimination

摘要: Widely used regression approaches in modeling quantitative structure-property relationships, such as PLS regression, are highly susceptible to outlying observations that will impair the prognostic value of a model. Our aim is compile homogeneous datasets basis for by removing compounds and applying variable selection. We investigate different create robust, outlier-resistant models field prediction drug molecules' permeability. The objective join strength outlier detection elimination increasing predictive power models. In conclusion, employed identify multiple, data subsets modeling.