Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

ABSTRACT: Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results

SUBMITTER: Fagerholm U 

PROVIDER: S-EPMC8124353 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature