Prediction

1. Draw molecules or select a file.

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ID Mol

2. Select prediction parameters.

Parameter Species/Cell Type Output Accuracy Reference Method Descriptor
D-Sol7.4 2 class
  • Low (< 10 μg/mL)
  • High (> 10 μg/mL)
  • Accuracy: 0.811
  • Kappa: 0.628
Esaki et al.1 L-SVM
fu,p Human 3 class
  • Low (0.001-0.05)
  • Medium (0.05-0.2)
  • High (0.2-1.0)
  • Accuracy: 0.676
  • True positive rate in Low class: 0.826
Watanabe et al.2 RF8
Regression Value R2 = 0.691 RF8
fu,p Rat 3 class
  • Low (0.001-0.05)
  • Medium (0.05-0.2)
  • High (0.2-1.0)
  • Kappa: 0.484
RF8
  • Mordred10
  • jCompoundMapper12
Regression Value R2 = 0.590 RF8
fu,brain Mammal Regression Value
  • R2 = 0.630
  • RMSE = 0.477
Esaki et al.3 Gradient Boosting
CLint Human 3 class
  • Stable (< 20 μl/min/mg)
  • Moderate (20-300 μl/min/mg)
  • Unstable (> 300 μl/min/mg)
  • Accuracy: 0.771
  • Kappa: 0.588
Esaki et al.4 R-SVM
  • Mordred10
  • jCompoundMapper12
CYP Human Probability Value Accuracy: CYP1A2: 0.617; CYP2C9: 0.600; CYP2D6: 0.712; CYP3A4: 0.825 RF8
CYP Human Site Site Yamazoe et al.5, 6
Papp Caco-2 2 class
  • Low (< 10-5 cm/s)
  • High (> 10-5 cm/s)
  • Accuracy: 0.810
  • Kappa: 0.601
Esaki et al.1 R-SVM
Papp LLC-PK1 Regression Value R2 = 0.687 Linear Stacking
(LGBM, XGB, Catboost, RF8, NN)
  • CDK9
  • Mordred10
  • jCompoundMapper12
  • RDKit13
NER LLC-PK1 3 class
  • Low (< 1.4)
  • Medium (1.4-9.5)
  • High (> 9.5)
kappa = 0.58 Gradient Boosting
  • Mordred10
  • jCompoundMapper12
Fa Human 3 class
  • Low (0-0.2)
  • Medium (0.2-0.7)
  • High (0.7-1.0)
  • Accuracy: 0.836
  • Kappa: 0.560
Esaki et al.1 RF8
CLr Human Regression Value In higher range of CLr (more than 0.0612 L/h/kg), 70.5% of samples were fell in within 2-fold error Watanabe et al.7
  • RF8
  • PLS
fe Human 2 class
  • Low (< 0.3)
  • Medium-High (> 0.3)
  • Kappa: 0.49
  • Balanced accuracy: 0.74
Watanabe et al.7 RF8
CR type Human 3 class
  • Reabsorption
  • Secretion
  • Intermediate
  • Kappa: 0.32
  • Balanced accuracy: 0.70, 0.58 and 0.68 in Reabsorption, Intermediate and Secretion, respectively
Watanabe et al.7 RF8

References

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Yamazoe, Y., Yoshinari, K., Prediction of regioselectivity and preferred order of metabolisms on CYP1A2-mediated reactions. Part 3. Difference in substrate specificity of human and rodent CYP1A2 and the refinement of predicting system. Drug Metab Pharmacokinet. 2019; 34(4):217-232.
  6. Yamazoe, Y., Goto, T., Tohkin, M., Reconstitution of CYP3A4 active site through assembly of ligand interactions as a grid-template: solving the modes of the metabolism and inhibition. Drug. Metab. Pharmacokinet. 2019; 34(2):113-125.
  7. 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.
  8. Breiman, L., Random Forest. Machine Learn. 2001; 45(1):5-32.
  9. 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.
  10. Moriwaki, H., Tian, Y. S., Kawashita, N., Takagi, T., Mordred: a molecular descriptor calculator. J. Cheminform. 2018; 10(1):4.
  11. Yap, C. W., PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 2011; 32(7):1466-1474.
  12. 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.
  13. RDKit: Open-source cheminformatics; http://www.rdkit.org