Abstract
Organic cation transporters (OCTs) are a small family of transmembrane proteins that regulate the pharmacokinetics (PK) of natural metabolites and xenobiotics by facilitating drug uptake and elimination. Measuring the modulation (either inhibition or substrate) of OCTs by small molecules requires expensive experiments. More cost-effective in silico models that accurately predict OCT-mediated uptake would enable the forecasting of potential PK liabilities of new drug candidates at an early stage. In this paper, we present new machine learning (ML) models to predict the uptake of OCT1, OCT2, and OCT3. Built using advanced decision tree ensemble algorithms and VolSurf molecular features, these models are based on the largest and most well-curated data sets available in the current literature. Several rounds of validation with different external test sets have confirmed the predictive power of these models, with Matthews correlation coefficient (MCC) values above 0.45. We believe that these models will shed new light on the impact of OCTs on drug discovery and development.