# Machine Learning Modeling for ABC Transporter Efflux and Inhibition: Data Curation, Model Development, and New Compound Interaction Predictions

## TL;DR

This study curates a large dataset of ABC transporter interactions and develops machine learning models to predict substrate binding and inhibition, which can help estimate drug exposure in the brain.

## Contribution

The study presents a large, manually curated dataset and high-performance machine learning models for predicting ABC transporter interactions.

## Key findings

- Eight datasets were created with around 8800 unique chemicals related to four ABC transporters.
- Machine learning models achieved average CCRs of 0.764 for substrate binding and 0.839 for inhibition.
- Compounds predicted as P-gp and BCRP substrates were more likely to have low brain exposure.

## Abstract

In recent years, multiple computational studies have
used machine
learning models to predict substrate binding and inhibition of ATP-binding
cassette (ABC) transporters. However, many of these studies relied
on relatively small training sets with limited applicability. In this
study, we manually curated over 24,000 bioactivity records (i.e.,
inhibition, binding affinity, permeability) for the ABC transporters
P-gp, BCRP, MRP1, and MRP2 from more than 900 literature sources in
ChEMBL, with additional data from PubChem and Metrabase. This effort
yielded eight data sets, comprising around 8800 unique chemicals with
one or more substrate binding or inhibition activities for these four
efflux transporters. Quantitative structure–activity relationship
(QSAR) models were developed for each of the eight data sets using
combinations of four machine learning algorithms and three sets of
chemical descriptors. The resulting models demonstrated excellent
performance by 5-fold cross-validation, achieving an average correct
classification rate (CCR) of 0.764 for the substrate binding models
and 0.839 for the inhibition models. Models were validated with additional
compounds from DrugBank that were known substrates or inhibitors.
We further analyzed how model predictions for efflux transporter activity
could estimate exposure of the brain to xenobiotics. Notably, compounds
predicted as P-gp and BCRP substrates were twice or more likely to
have low brain exposure compared to compounds with high brain exposure.
This study provides a large and curated drug transporter binding and
inhibition database for computational modeling. Applicable models
based on this large database for predicting transporter substrate
binding and inhibition can be used to evaluate more complex drug bioactivities,
such as exposure of protected tissues to chemicals.