In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning

Predicting the fraction unbound of a drug in plasma plays a significant
role in understanding its pharmacokinetic properties during in vitro
studies of drug design and discovery. Owing to the gaining reliability
of machine learning in biological predictive models and development
of automated machine learning techniques for the ease of nonexperts
of machine learning to optimize and maximize the reliability of the
model, in this experiment, we built an in silico prediction model
of a fraction unbound drug in human plasma using a chemical fingerprint
and a freely available AutoML framework. The predictive model was
trained on one of the largest data sets ever of 5471 experimental
values using four different AutoML frameworks to compare their performance
on this problem and to choose the most significant one. With a coefficient
of determination of 0.85 on the test data set, our best prediction
model showed better performance than other previously published models,
giving our model significant importance in pharmacokinetic modeling.