Prediction

1. Draw molecules or select a file.

Update Add to list
File format
Title Mol SMILES 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 RF13
fu,p Human Regression Value R2 = 0.691 RF13
fu,p.class Rat 3 class
  • Low (0.001-0.05)
  • Medium (0.05-0.2)
  • High (0.2-1.0)
  • Kappa: 0.484
 RF13
  • Mordred11
  • jCompoundMapper14
fu,p Rat Regression Value
  • MSE: 1.70
  • R2: 0.51
 Watanabe et al.3 GB 2021/03
fu,brain Mammal Regression Value
  • MSE: 1.48
  • R2: 0.58
 Watanabe et al.3 GB
  • Mordred11
  • jCompoundMapper14
 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
  • jCompoundMapper14
CLint6 Human Regression Value Accuracy: 0.7882 LGBM
  • Mordred11
  • jCompoundMapper14
CYP.probability7 Human Probability Value Accuracy:
  • CYP1A2: 0.617
  • CYP2C9: 0.600
  • CYP2D6: 0.712
  • CYP3A4: 0.825
 RF13
CYP.site7 Human Site Site Yamazoe et al.8, 9
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) Human/LLC-PK1 Regression Value R2 = 0.687 Linear Stacking
(LGBM, XGB, Catboost, RF13, NN)
  • CDK10
  • Mordred11
  • jCompoundMapper14
  • RDKit15
P-gp NER.class Human/LLC-PK1 3 class
  • Low (1-1.4)
  • Medium (1.4-9.8)
  • High (> 9.8)
 Kappa: 045 Watanabe et al.3 GB
  • Mordred11
  • jCompoundMapper14
 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 RF6
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.5
  • RF13
  • PLS
fe.class Human 2 class
  • Low (< 0.3)
  • Medium-High (> 0.3)
  • Kappa: 0.49
  • Balanced accuracy: 0.74
 Watanabe et al.5 RF13
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.5 RF13
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.3 GB
  • Mordred11
  • jCompoundMapper14
  • ChemAxon16 if the pKa values are not given by the user.

Citing the individual models

  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. 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.
  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. 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. This prediction model was developed in collaboration with SyntheticGestalt, Ltd. (Tokyo, Japan).
  7. These prediction models were developed and donated by Fujitsu, Ltd. (Tokyo, Japan).
  8. 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.
  9. 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.

Other references

  1. 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.
  2. Moriwaki, H., Tian, Y. S., Kawashita, N., Takagi, T., Mordred: a molecular descriptor calculator. J. Cheminform. 2018; 10(1):4.
  3. Yap, C. W., PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 2011; 32(7):1466-1474.
  4. Breiman, L., Random Forest. Machine Learn. 2001; 45(1):5-32.
  5. 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.
  6. RDKit: Open-source cheminformatics; https://www.rdkit.org
  7. 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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