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An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction
- Source :
- Molecular pharmaceutics. 16(2)
- Publication Year :
- 2018
-
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.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Generalization
education
Pharmaceutical Science
Multi-task learning
Quantitative Structure-Activity Relationship
Machine Learning (stat.ML)
02 engineering and technology
Machine learning
computer.software_genre
030226 pharmacology & pharmacy
Machine Learning (cs.LG)
03 medical and health sciences
0302 clinical medicine
Statistics - Machine Learning
Drug Discovery
Learning
Pharmacokinetics
ADME
business.industry
Deep learning
021001 nanoscience & nanotechnology
Bioavailability
Data set
Molecular Medicine
Artificial intelligence
Neural Networks, Computer
0210 nano-technology
business
Transfer of learning
computer
Predictive modelling
Algorithms
Subjects
Details
- ISSN :
- 15438392
- Volume :
- 16
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Molecular pharmaceutics
- Accession number :
- edsair.doi.dedup.....83b9cda0d5e6f4431d05437c926cb949