Prediction

1. Draw molecules or select a file.

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ID Mol

2. Select prediction parameters.

Parameter Organism/Cell Type Output Accuracy Reference Method Descriptor
Sol(7.4).class 2 class
  • Low (< 10 μg/mL)
  • High (> 10 μg/mL)
  • Accuracy: 0.811
  • Kappa: 0.628
Esaki et al.1 L-SVM
fu,p.class 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
fu,p Human Regression Value R2 = 0.691 RF8
fu,p.class Rat 3 class
  • Low (0.001-0.05)
  • Medium (0.05-0.2)
  • High (0.2-1.0)
  • Kappa: 0.484
RF8
  • Mordred10
  • jCompoundMapper12
fu,p Rat Regression Value R2 = 0.590 RF8
fu,brain Mammal Regression Value
  • R2 = 0.630
  • RMSE = 0.477
Esaki et al.3 Gradient Boosting
CLint.class 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
CLint14 Human Regression Value Accuracy: 0.7882 LGBM
  • Mordred10
  • jCompoundMapper12
CYP.probability15 Human Probability Value Accuracy: CYP1A2: 0.617; CYP2C9: 0.600; CYP2D6: 0.712; CYP3A4: 0.825 RF8
CYP.site15 Human Site Site Yamazoe et al.5, 6
Papp(AtoB).class 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(AtoB)16 LLC-PK1 Regression Value R2 = 0.687 Linear Stacking
(LGBM, XGB, Catboost, RF8, NN)
  • CDK9
  • Mordred10
  • jCompoundMapper12
  • RDKit13
NER.class LLC-PK1 3 class
  • Low (< 1.4)
  • Medium (1.4-9.5)
  • High (> 9.5)
kappa = 0.58 Gradient Boosting
  • Mordred10
  • jCompoundMapper12
Fa.class 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 (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.class Human 2 class
  • Low (< 0.3)
  • Medium-High (> 0.3)
  • Kappa: 0.49
  • Balanced accuracy: 0.74
Watanabe et al.7 RF8
CR_type.class 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
  14. This prediction model was developed by SyntheticGestalt, Ltd. (Tokyo, Japan).
  15. These prediction models were developed by Fujitsu Kyushu Systems, Ltd. (Fukuoka, Japan).
  16. This prediction model was developed by Lifematics, Inc. (Tokyo, Japan).