Prediction Models for Fraction of Absorption and Membrane Permeability using Mordred Descriptors

Machine learning (ML) models are cost-effective methods that have accelerated the identification of novel drug candidates in pharmaceutical research. These silico estimate characteristics chemical compounds using calculated physicochemical features or molecular sub-structure fingerprints. This rise deployment machine is facilitated by development numerous ML packages enables researchers to build local meet their requirements. Despite growing ease building models, programming these informatics-driven solutions can be arduous for wet-lab researchers. In this study, we present a template model construction would enable efficiently reproduce models. We constructed prototype fraction absorption and membrane permeability compound Mordred descriptors.