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

Abstract

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