ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

Ning Ning Wang, Jie Dong, Yin Hua Deng, Min Feng Zhu, Ming Wen, Zhi Jiang Yao, Aiping LYU, Jian Bing Wang, Dong Sheng Cao*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

143 Citations (Scopus)


The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure-property relationship (QSPR) study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R2 = 0.97, RMSEF = 0.12, Q2 = 0.83, RMSECV = 0.31 for the training set and RT2 = 0.81, RMSET = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development.

Original languageEnglish
Pages (from-to)763-773
Number of pages11
JournalJournal of Chemical Information and Modeling
Issue number4
Publication statusPublished - 25 Apr 2016

Scopus Subject Areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences


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