An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction

TL;DR

This study introduces an integrated transfer learning and multitask learning framework that significantly improves the accuracy of pharmacokinetic parameter predictions for drug discovery, leveraging large bioactivity datasets and advanced data splitting techniques.

Contribution

The paper presents a novel combined transfer and multitask learning approach with an improved dataset splitting algorithm for pharmacokinetic prediction.

Findings

Enhanced prediction accuracy for four pharmacokinetic parameters.

Effective use of large bioactivity datasets for model training.

Improved dataset splitting method for better model generalization.

Abstract

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.