Zhuyifan Ye1, Yilong Yang1,2, Xiaoshan Li2, Dongsheng Cao3, Defang Ouyang1. 1. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS) , University of Macau , Macau , China. 2. Department of Computer and Information Science, Faculty of Science and Technology , University of Macau , Macau , China. 3. Xiangya School of Pharmaceutical Sciences , Central South University , No. 172, Tongzipo Road , Yuelu District, Changsha 410083 , People's Republic of China.
Abstract
BACKGROUND: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and 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 data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. RESULTS: The pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing 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 improve the model generalization. CONCLUSIONS: The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.
BACKGROUND: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and 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 data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. RESULTS: The pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing 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 improve the model generalization. CONCLUSIONS: The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.
Entities:
Keywords:
ADME; deep learning; multitask learning; pharmacokinetic parameters; transfer learning
Authors: Pius T Mpiana; Koto-Te-Nyiwa Ngbolua; Damien S T Tshibangu; Jason T Kilembe; Benjamin Z Gbolo; Domaine T Mwanangombo; Clement L Inkoto; Emmanuel M Lengbiye; Clement M Mbadiko; Aristote Matondo; Gedeon N Bongo; Dorothée D Tshilanda Journal: Chem Phys Lett Date: 2020-06-30 Impact factor: 2.328