Oral administration is the preferred and major route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The human colon carcinoma cell monolayer (Caco-2) has been used frequently as a simplified in vitro model of intestinal absorption. A nonlinear quantitative structure–activity relationship (QSAR) model was built in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to analysis and construct the extremely complicated relationships between descriptors and Caco-2 permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 104, r2 = 0.91, = 0.81, RMSE = 0.20, s = 0.18), test set (n = 26, q2 = 0.70–0.85, RMSE = 0.38, s = 0.20), and outlier set (n = 14, q2 = 0.76–0.95, RMSE = 0.24, s = 0.17). When subjected to a variety of statistical validations, the developed HSVR model consistently passed strictest criteria. Furthermore, the mock test can justify the predictivity of HSVR. As a result, this HSVR model can be adopted to promote drug discovery and development.

CONTENTS i
ABSTRACT iii
FIGURE v
TABLE vii
1. Introduction 1
2. Material and method 5
2.1 Data collection 5
2.2 Molecular descriptors 5
2.3 Descriptor selection 6
2.4 Dataset selection 7
2.5 Support Vector Regression 7
2.6 Hierarchical Support Vector Regression 8
2.7 Predictive Evaluation 8
2.8 Classification 11
3. Results 13
3.1 Dataset selection 13
3.2 SVR models 13
3.3 HSVR model 15
3.4 Predictive Evaluations 17
3.5 Mock test 17
3.6 Classification 18
4. Discussion 21
5. Conclusion 27
6. REFERENCES 29
7. Figure 41
8. Table 59
9. Table S1 67

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