  Background: Pharmacokinetic evaluation is one of the key processes in drug
discovery and development. However, current absorption, distribution,
metabolism, excretion prediction models still have limited accuracy. Aim: This
study aims to construct an integrated transfer learning and multitask learning
approach for developing quantitative structure-activity relationship models to
predict four human pharmacokinetic parameters. Methods: A pharmacokinetic
dataset included 1104 U.S. FDA approved small molecule drugs. The dataset
included four human pharmacokinetic parameter subsets (oral bioavailability,
plasma protein binding rate, apparent volume of distribution at steady-state
and elimination half-life). The pre-trained model was trained on over 30
million bioactivity data. An integrated transfer learning and multitask
learning approach was established to enhance the model generalization. Results:
The pharmacokinetic dataset was split into three parts (60:20:20) for training,
validation and test by the improved Maximum Dissimilarity algorithm with the
representative initial set selection algorithm and the weighted distance
function. The multitask learning techniques enhanced the model predictive
ability. The integrated transfer learning and multitask learning model
demonstrated the best accuracies, because deep neural networks have the general
feature extraction ability, transfer learning and multitask learning improved
the model generalization. Conclusions: The integrated transfer learning and
multitask learning approach with the improved dataset splitting algorithm was
firstly introduced to predict the pharmacokinetic parameters. This method can
be further employed in drug discovery and development.
