Prediction

1. Draw molecules or select a file.

Add to list
Title Mol Optional parameters
apKa bpKa logP

2. Select prediction parameters.

Parameter Organism/Cell Type Output Accuracy Reference Method Descriptor Updated at
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 RF9
fu,p Human Regression Value R2 = 0.691 RF9
fu,p.class Rat 3 class
  • Low (0.001-0.05)
  • Medium (0.05-0.2)
  • High (0.2-1.0)
  • Kappa: 0.484
RF9
  • Mordred11
  • jCompoundMapper13
fu,p Rat Regression Value
  • MSE: 1.70
  • R2: 0.51
Watanabe et al.8 GB 2021/03
fu,brain Mammal Regression Value
  • MSE: 1.48
  • R2: 0.58
Watanabe et al.8 GB
  • Mordred11
  • jCompoundMapper13
2021/03
The older version is here
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
  • Mordred11
  • jCompoundMapper13
CLint15 Human Regression Value Accuracy: 0.7882 LGBM
  • Mordred11
  • jCompoundMapper13
CYP.probability 16 Human Probability Value Accuracy:
  • CYP1A2: 0.617
  • CYP2C9: 0.600
  • CYP2D6: 0.712
  • CYP3A4: 0.825
RF9
CYP.site16 Human Site Site Yamazoe et al.5, 6
Papp(AtoB).class Caco-2 2 class
  • Low (< 100 nm/s)
  • High (> 100 nm/s)
  • Accuracy: 0.810
  • Kappa: 0.601
Esaki et al.1 R-SVM
Papp(AtoB)17 Human/LLC-PK1 Regression Value R2 = 0.687 Linear Stacking
(LGBM, XGB, Catboost, RF9, NN)
  • CDK10
  • Mordred11
  • jCompoundMapper13
  • RDKit14
P-gp NER.class Human/LLC-PK1 3 class
  • Low (< 1.4)
  • Medium
  • High (> 9.8)
Kappa: 045 Watanabe et al.8 GB
  • Mordred11
  • jCompoundMapper13
2021/03
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 RF9
CLr Human Regression Value In higher range of clr_human (more than 1.02 mL/min/kg), 70.5% of samples were fell in within 2-fold error Watanabe et al.7
  • RF9
  • PLS
fe.class Human 2 class
  • Low (< 0.3)
  • Medium-High (> 0.3)
  • Kappa: 0.49
  • Balanced accuracy: 0.74
Watanabe et al.7 RF9
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 RF9
Kp,brain
Kp,uu,brain
Rat Correction method considering P-gp NER Value Kp,uu,brain
42.9% and 64.3% samples were fell within 5- and 10-fold, respectively.
Watanabe et al.8 GB
  • Mordred11
  • jCompoundMapper13
  • ChemAxon18 if the pKa values are not given by the user.

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. Watanabe, R., Esaki, T., Ohashi, R., Kuroda, M., Kawashima, H., Komura, H., Natsume-Kitatani, Y., Mizuguchi, K. Development of an in silico prediction model for P-glycoprotein efflux potential in brain capillary endothelial cells towards the prediction of brain penetration. J. Med. Chem. 2021; 64(5):2725-2738.
  9. Breiman, L., Random Forest. Machine Learn. 2001; 45(1):5-32.
  10. 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.
  11. Moriwaki, H., Tian, Y. S., Kawashita, N., Takagi, T., Mordred: a molecular descriptor calculator. J. Cheminform. 2018; 10(1):4.
  12. Yap, C. W., PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 2011; 32(7):1466-1474.
  13. 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.
  14. RDKit: Open-source cheminformatics; https://www.rdkit.org
  15. This prediction model was developed by SyntheticGestalt, Ltd. (Tokyo, Japan).
  16. These prediction models were developed by Fujitsu Kyushu Systems, Ltd. (Fukuoka, Japan).
  17. This prediction model was developed by Lifematics, Inc. (Tokyo, Japan).
  18. Protonation calculator was used for the Kp,brain and Kp,uu,brain predictions, Marvin 21.3.0, ChemAxon (https://www.chemaxon.com)
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