Prediction

1. Draw molecules or select a file.

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ID Mol

2. Select prediction parameters.

Model Type Output Dataset Accuracy Method Descriptor
fu,p class 3 class
  • Low (0.001-0.05)
  • Medium (0.05-0.2)
  • High (0.2-1.0)
Watanabe et al.1
  • Accuracy: 0.676
  • True positive rate in Low class: 0.826
RF6
  • Mordred9
  • PaDEL10
fu,p Regression fu,p value R2= 0.691 RF6
fu,brain Regression fu,brain value Esaki et al.2
  • R2= 0.630
  • RMSE= 0.477
Gradient Boosting
  • CDK7
  • Mordred9
  • PaDEL10
CLint 3 class
  • Stable (< 20 μl/min/mg)
  • Moderate (20-300 μl/min/mg)
  • Unstable (> 300 μl/min/mg)
Esaki et al.3
  • Accuracy: 0.771
  • Kappa: 0.588
R-SVM
  • jCompoundMapper8
  • Mordred9
Fa class 3 class
  • Low (0-0.2)
  • Moderate (0.2-0.7)
  • High (0.7-1.0)
Esaki et al.4
  • Accuracy: 0.836
  • Kappa: 0.560
RF6
  • CDK7
  • Mordred9
  • PaDEL10
Papp class 2 class
  • Low (< 10-5 cm/s)
  • High (> 10-5 cm/s)
  • Accuracy: 0.810
  • Kappa: 0.601
R-SVM
D-Sol class 2 class
  • Low (< 10 μg/mL)
  • High (> 10 μg/mL)
  • Accuracy: 0.811
  • Kappa: 0.628
L-SVM
fe class 2 class
  • Low (< 0.3)
  • Medium-High (> 0.3)
Watanabe et al.5
  • Kappa: 0.49
  • Balanced accuracy: 0.74
RF6
  • Mordred9
  • PaDEL10
CR type 3 class
  • Reabsorption
  • Intermediate
  • Secretion
  • Kappa: 0.32
  • Balanced accuracy: 0.70, 0.58 and 0.68 in Reabsorption, Intermediate and Secretion, respectively
RF6
CLr Regression
  • CLrvalue
  • In higher range of CLr (more than 0.0612 L/h/kg), 70.5% of samples were fell in within 2-fold error
  • RF6
  • PLS

References

  1. Watanabe, R., Esaki, T., Kawashima, H., Natsume-Kitatani, Y., Nagao, C., Ohashi, R., Mizuguchi, K., Predicting fraction unbound in human plasma from chemical structure: improved accuracy in the low value ranges. Mol. Pharm. 2018; 15(11):5302-5311.
  2. Esaki, T., Ohashi, R., Watanabe, R., Natsume-Kitatani, Y., Kawashima, H., Nagao, C., Mizuguchi, K., Computational model to predict the fraction of unbound drug in the brain. J. Chem. Inform. Model. 2019; 59(7):3251-3261.
  3. Esaki, T., Watanabe, R., Kawashima, H., Ohashi, R., Natsume-Kitatani, Y., Nagao, C., Mizuguchi, K., Data curation can improve the prediction accuracy of metabolic intrinsic clearance. Mol. Inform. 2019; 38(1-2):e1800086.
  4. Esaki, T., Ohashi, R., Watanabe, R., Natsume-Kitatani, Y., Kawashima, H., Nagao, C., Komura, H., Mizuguchi, K., Constructing an in silico three-class predictor of human intestinal absorption with Caco-2 permeability and dried-DMSO solubility. J. Pharm. Sci. 2019; 108(11):3630-3639.
  5. Watanabe, R., Ohashi, R., Esaki, T., Kawashima, H., Natsume-Kitatani, Y., Nagao, C., Mizuguchi, K.,Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor. Sci. Rep. 2019; 9(1):18782.
  6. Breiman, L., Random Forest. Machine Learn. 2001; 45(1):5-32.
  7. Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., Willighagen, E., The Chemistry Development Kit (CDK): an open-source Java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 2003; 43(2):493-500.
  8. Hinselmann, G., Rosenbaum, L., Jahn, A., Fechner, N., Zell, A., jCompoundMapper: an open source Java library and command-line tool for chemical fingerprints. J. Cheminform. 2011; 3(1):3.
  9. Moriwaki, H., Tian, Y. S., Kawashita, N., Takagi, T., Mordred: a molecular descriptor calculator. J. Cheminform. 2018; 10(1):4.
  10. Yap, C. W., PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 2011; 32(7):1466-1474.